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Recent questions and answers in Artificial Intelligence
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GATE DS&AI 2024 | Question: 13
Let $h_{1}$ and $h_{2}$ be two admissible heuristics used in $A^{*}$ search. Which ONE of the following expressions is always an admissible heuristic? $h_{1}+h_{2}$ $h_{1} \times h_{2}$ $h_{1} / h_{2},\left(h_{2} \neq 0\right)$ $\left|h_{1}-h_{2}\right|$
NarutoUzumaki
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Artificial Intelligence
Feb 16
by
NarutoUzumaki
787
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gate-ds-ai-2024
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Memory Based GATE DA 2024 | Question: 32
Consider two admissible heuristic functions, \(h_1\) and \(h_2\). Determine which of the following combinations are admissible: \(\frac{h_1}{h_2}\) \(\left(h_2 > 0\right)\) \\ \(h_1 \cdot \tilde{h}_2\) \\ \(\left| h_1 - h_2 \right|\) \\ \(h_1 + h_2\)
GO Classes
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Feb 4
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GO Classes
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Memory Based GATE DA 2024 | Question: 50
You are provided with three images, each depicting a different face of a six-sided dice. Based on these images, determine the correct option.
GO Classes
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Artificial Intelligence
Feb 4
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GO Classes
122
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gate2024-da-memory-based
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artificial-intelligence
0
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UPENN | ML | Cross validation
Suppose you have picked the parameter \( \theta \) for a model using 10-fold cross-validation. The best way to pick a final model to use and estimate its error is to (a) pick any of the 10 models you built for your model; use its error estimate on ... a new model on the full data set, using the \( \theta \) you found; use the average CV error as its error estimate
squirrel69
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Artificial Intelligence
Jan 31
by
squirrel69
256
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machine-learning
artificial-intelligence
statistics
0
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0
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What resources can i use to study the Data Warehousing part for the GATE DA paper?
Ameya Kulkarni
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Artificial Intelligence
Jan 30
by
Ameya Kulkarni
149
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2
votes
2
answers
6
DRDO CSE 2022 Paper 2 | Question: 31
What is the State $\mathrm{X}$ called for the following machine learning model?
kaptaan_11
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in
Artificial Intelligence
Jan 27
by
kaptaan_11
673
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drdocse-2022-paper2
artificial-intelligence
2-marks
descriptive
0
votes
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DA Practice | UPENN | ML | Naive Bais
Suppose you have a three-class problem where class label \( y \in \{0, 1, 2\} \), and each training example \( \mathbf{X} \) has 3 binary attributes \( X_1, X_2, X_3 \in \{0, 1\} \). How many parameters do you need to know to classify an example using the Naive Bayes classifier? (a) 5 b) 9 (c) 11 (d) 13 (e) 23
ruchit816
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in
Artificial Intelligence
Jan 23
by
ruchit816
391
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machine-learning
artificial-intelligence
statistics
probability
0
votes
1
answer
8
UPENN | ML Questions for GATE DA
In fitting some data using radial basis functions with kernel width $σ$, we compute training error of $345$ and a testing error of $390$. (a) increasing $σ$ will most likely reduce test set error (b) decreasing $σ$ will most likely reduce test set error (C) not enough information is provided to determine how $σ$ should be changed
ruchit816
answered
in
Artificial Intelligence
Jan 23
by
ruchit816
258
views
machine-learning
statistics
artificial-intelligence
0
votes
1
answer
9
UPENN | ML | DA Practice | Regularization
After applying a regularization penalty in linear regression, you find that some of the coefficients of $w$ are zeroed out. Which of the following penalties might have been used? (a) L0 norm (b) L1 norm (c) L2 norm (d) either (A) or (B) (e) any of the above
ruchit816
answered
in
Artificial Intelligence
Jan 21
by
ruchit816
265
views
machine-learning
artificial-intelligence
statistics
0
votes
1
answer
10
AI Sample Question for DS-AI
Imagine you are guiding a robot through a grid-based maze using the A* algorithm. The robot is currently at node A (start) and wants to reach node B (goal). The heuristic function $h(n)$ is the Euclidean distance between a node and the goal. The ... algorithm explore next based on the A* calculation? A) Node C B) Node D C) Node E D) Not enough information to decide
rajveer43
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in
Artificial Intelligence
Jan 16
by
rajveer43
378
views
artificial-intelligence
machine-learning
probability
statistics
0
votes
1
answer
11
UPENN | DS-AI Sample | Decision Tree
When choosing one feature from \(X_1, \ldots, X_n\) while building a Decision Tree, which of the following criteria is the most appropriate to maximize? (Here, \(H()\) means entropy, and \(P()\) means probability) (a) \(P(Y | X_j)\) (b) \(P(Y) - P(Y | X_j)\) (c) \(H(Y) - H(Y | X_j)\) (d) \(H(Y | X_j)\) (e) \(H(Y) - P(Y)\)
rajveer43
answered
in
Artificial Intelligence
Jan 16
by
rajveer43
221
views
artificial-intelligence
machine-learning
statistics
probability
0
votes
1
answer
12
UPENN | ML | DECISION TREE
Given the following table of observations, calculate the information gain $IG(Y |X)$ that would result from learning the value of $X$. X Y Red True Green False Brown False Brown False (a) 1/2 (b) 1 (c) 3/2 (d) 2 (e) none of the above
rajveer43
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in
Artificial Intelligence
Jan 16
by
rajveer43
216
views
artificial-intelligence
statistics
machine-learning
binary-tree
0
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1
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13
Decision Tree | Sample Question
$True$ or $False?$ If decision trees such as the ones we built in class are allowed to have decision nodes based on questions that can have many possible answers (e.g. “What country are you from) in addition to binary questions, they will in general tend to add the multiple answer questions to the tree before adding the binary questions
prasantkr.singh
answered
in
Artificial Intelligence
Jan 15
by
prasantkr.singh
219
views
algorithms
artificial-intelligence
machine-learning
0
votes
1
answer
14
UPENN | ML | Cross Validation
P1: In the limit of infinite training and test data, consistent estimators always give at least as low a test error as biased estimators. P2: Leave-one out cross validation (LOOCV) generally gives less accurate estimates of true test error than 10-fold ... following Statements is/are correct? Only P1 is True Only P2 is True P1 is True and P2 is False Both are False
rajveer43
answered
in
Artificial Intelligence
Jan 13
by
rajveer43
194
views
machine-learning
artificial-intelligence
statistics
0
votes
0
answers
15
UPENN | ML | DA Practice
Using the same data as above \( \mathbf{X} = [-3, 5, 4] \) and \( \mathbf{Y} = [-10, 20, 20] \), assuming a ridge penalty \( \lambda = 50 \), what ratio versus the MLE estimate \( \hat{\mathbf{w}}_{\text{MLE}} \) do you think the ridge regression \( L_2 \) estimate \( \hat{\mathbf{w}}_{\text{ridge}} \) will be? (a)] 2 b)] 1 (c)] 0.666 (d)] 0.5
rajveer43
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in
Artificial Intelligence
Jan 13
by
rajveer43
127
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artificial-intelligence
machine-learning
statistics
0
votes
1
answer
16
UPENN | ML | DA Practice
Consider the statements: $P1:$ It is generally more important to use consistent estimators when one has smaller numbers of training examples. $P2:$ It is generally more important to used unbiased estimators when one has smaller numbers of training examples. Which of the following statement( ... $P1$ and $P2$ are true (C) Only $P2$ is True (D) Both $P1$ and $P2$ are False
rajveer43
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in
Artificial Intelligence
Jan 13
by
rajveer43
133
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machine-learning
artificial-intelligence
statistics
0
votes
1
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17
DA Practice | UPENN | ML | Bias-Variance Trade Off | Regularization
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + k \|\mathbf{w}\|_p^p. \] What is the effect of increasing \( p ... , decreases variance (c)] Decreases bias, increases variance (d)] Decreases bias, decreases variance (e)] Not enough information to tell
rajveer43
answered
in
Artificial Intelligence
Jan 13
by
rajveer43
154
views
machine-learning
artificial-intelligence
statistics
0
votes
1
answer
18
UPENN | ML | DA Practice | Bias-Variance Trade-Off
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + \lambda \|\mathbf{w}\|_1. \] What is the effect of increasing \( \lambda \) ... bias, decreases variance (c)] Decreases bias, increases variance (d)] Decreases bias, decreases variance (e)] Not enough information to tell
rajveer43
answered
in
Artificial Intelligence
Jan 13
by
rajveer43
121
views
artificial-intelligence
machine-learning
statistics
0
votes
1
answer
19
UPENN | Midterm | K Fold Validation | DA Practice |
Suppose we want to compute $10-Fold$ Cross-Validation error on $100$ training examples. We need to compute error $N1$ times, and the Cross-Validation error is the average of the errors. To compute each error, we need to build a model with data of size $N2$, and test the ... $N1 = 10, N2 = 100, N3 = 10$ (d) $N1 = 10, N2 = 100, N3 = 10$
rajveer43
answered
in
Artificial Intelligence
Jan 13
by
rajveer43
121
views
machine-learning
artificial-intelligence
3
votes
3
answers
20
ISRO2018-75
ln neural network, the network capacity is defined as: The traffic (tarry capacity of the network The total number of nodes in the network The number of patterns that can be stored and recalled in a network None of the above
rajveer43
answered
in
Artificial Intelligence
Jan 3
by
rajveer43
2.6k
views
isro2018
non-gate
neural-network
0
votes
2
answers
21
UGC NET CSE | October 2020 | Part 2 | Question: 36
Which of the following is NOT true in problem solving in artificial intelligence? Implements heuristic search technique Solution steps are not explicit Knowledge is imprecise It works on or implements repetition mechanism
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
1.4k
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ugcnetcse-oct2020-paper2
non-gate
artificial-intelligence
1
vote
2
answers
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UGC NET CSE | June 2012 | Part 3 | Question: 21
$A^*$ algorithm uses $f'=g+h'$ to estimate the cost of getting from the initial state to the goal state, where $g$ is a measure of cost getting from initial state to the current node and the function $h'$ is an estimate of the cost of getting from the ... . To find a path involving the fewest number of steps, we should test, $g=1$ $g=0$ $h'=0$ $h'=1$
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
4.4k
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ugcnetcse-june2012-paper3
artificial-intelligence
3
votes
2
answers
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UGC NET CSE | June 2012 | Part 3 | Question: 2
In Delta Rule for error minimization weights are adjusted w.r.to change in the output weights are adjusted w.r.to difference between desired output and actual output weights are adjusted w.r.to difference between output and output none of the above
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
5.1k
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ugcnetcse-june2012-paper3
artificial-intelligence
machine-learning
0
votes
2
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24
UGC NET CSE | December 2012 | Part 2 | Question: 46
Back propagation is a learning technique that adjusts weights in the neutral network by propagating weight changes. Forward from source to sink Backward from sink to source Forward from source to hidden nodes Backward from sink to hidden nodes
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
12.7k
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ugcnetcse-dec2012-paper2
machine-learning
data-mining
back-propagation
3
votes
2
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25
UGC NET CSE | December 2015 | Part 3 | Question: 8
Forward chaining systems are ____ where as backward chaining systems are ____ Data driven, Data driven Goal driven, Data driven Data driven, Goal driven Goal driven, Goal driven
rajveer43
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Artificial Intelligence
Jan 3
by
rajveer43
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ugcnetcse-dec2015-paper3
artificial-intelligence
chaining
1
vote
2
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UGC NET CSE | December 2015 | Part 3 | Question: 45
Reasoning strategies used in expert systems include Forward chaining, backward chaining and problem reduction Forward chaining, backward chaining and boundary mutation Forward chaining, backward chaining and back propagation Forward chaining, problem reduction and boundary mutation
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
2.5k
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ugcnetcse-dec2015-paper3
artificial-intelligence
expert-system
3
votes
2
answers
27
UGC NET CSE | December 2015 | Part 3 | Question: 46
Language model used in LISP is Functional programming Logic programming Object oriented programming All of the above
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
1.8k
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ugcnetcse-dec2015-paper3
artificial-intelligence
0
votes
1
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28
Artificial Intelligence Heuristic problem Confusion
rajveer43
answered
in
Artificial Intelligence
Jan 3
by
rajveer43
537
views
artificial
intelligence
0
votes
2
answers
29
UGC NET CSE | July 2018 | Part 2 | Question: 73
In heuristic search algorithms in Artificial Intelligence (AI), if a collection of admissible heuristics $h_1 \dots h_m$ is available for a problem and none of them dominates any of the others, which should we choose? $h(n)=max\{h_1(n), \dots , h_m(n)\}$ ... $h(n)=avg\{h_1(n), \dots , h_m(n)\}$ $h(n)=sum\{h_1(n), \dots , h_m(n)\}$
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
2.0k
views
ugcnetcse-july2018-paper2
artificial-intelligence
0
votes
2
answers
30
UGC NET CSE | July 2018 | Part 2 | Question: 78
Consider the following two sentences: The planning graph data structure can be used to give a better heuristic for a planning problem Dropping negative effects from every action schema in a planning problem results in a relaxed problem Which of the ... b are true Sentence a is true but sentence b is false Sentence a is false but sentence b is true
rajveer43
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Artificial Intelligence
Jan 3
by
rajveer43
1.6k
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ugcnetcse-july2018-paper2
planning
1
vote
2
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31
UGC NET CSE | June 2019 | Part 2 | Question: 97
Consider the following: Evolution Selection Reproduction Mutation Which of the following are found in genetic algorithms? b, c and d only b and d only a, b, c and d a, b and d only
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
6.3k
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ugcnetcse-june2019-paper2
artificial-intelligence
genetic-algorithms
0
votes
4
answers
32
UGC NET CSE | June 2016 | Part 3 | Question: 66
A perceptron has input weights $W_1=-3.9$ and $W_2=1.1$ with threshold value $T=0.3.$ What output does it give for the input $x_1=1.3$ and $x_2=2.2?$ $-2.65$ $-2.30$ $0$ $1$
rajveer43
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in
Artificial Intelligence
Jan 3
by
rajveer43
1.3k
views
ugcnetcse-june2016-paper3
0
votes
4
answers
33
UGC NET CSE | July 2018 | Part 2 | Question: 74
Consider following sentences regarding $A^*$, an informed search strategy in Artificial Intelligence (AI). $A^*$ expands all nodes with $f(n)<C^*$ $A^*$ expands no nodes with $f(n) \geq C^*$ Pruning is integral to $A^*$ ... Both statements a and statement c are true Both statements b and statement c are true All the statements a, b and c are true
rajveer43
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Artificial Intelligence
Jan 3
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rajveer43
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ugcnetcse-july2018-paper2
artificial-intelligence
1
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1
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34
Machine Learning
You are a designing a machine learning model for a binary classification problem. The model has three features: f1, f2, f3. Derive the objective and loss function for this problem.
rajveer43
answered
in
Artificial Intelligence
Jan 3
by
rajveer43
434
views
machine-learning
0
votes
1
answer
35
Machine Learning Self-doubt
Please Solve this question with full explanation.
rajveer43
answered
in
Artificial Intelligence
Jan 3
by
rajveer43
254
views
machine-learning
self-doubt
0
votes
1
answer
36
Ai Questions | DS-AI Paper | GATE 2024
Given a tree with a branching factor of 3 and a depth of 4, calculate the maximum number of nodes expanded during a breadth-first search.
C.Aravind REDDY
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in
Artificial Intelligence
Jan 2
by
C.Aravind REDDY
345
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discrete-mathematics
analytical-aptitude
quantitative-aptitude
artificial-intelligence
1
vote
1
answer
37
DRDO CSE 2022 Paper 2 | Question: 28 (a)
Provide the correct answer for the following: ________ is not the best evaluation metric for cancer prediction problem.
Tejas07
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in
Artificial Intelligence
Dec 29, 2023
by
Tejas07
603
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drdocse-2022-paper2
artificial-intelligence
2-marks
fill-in-the-blanks
0
votes
0
answers
38
GATE DS-AI questions | ML
Consider the feature transform z = [L0(x) L1(x) L2(x)]T with Legendre polynomials and the linear model h(x) = w T .z. For the regularized hypothesis with w = [−1 + 2 − 1] T , what is h(x) explicitly as a function of x? write solution for It.
rajveer43
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Artificial Intelligence
Dec 11, 2023
by
rajveer43
332
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artificial-intelligence
machine-learning
1
vote
1
answer
39
DRDO CSE 2022 Paper 2 | Question: 28 (b)
Provide the correct answer for the following: The phenomena in which training error of the model decreases but test error increases is called___________.
Lakshay Kakkar
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in
Artificial Intelligence
Dec 3, 2023
by
Lakshay Kakkar
522
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drdocse-2022-paper2
artificial-intelligence
2-marks
fill-in-the-blanks
1
vote
1
answer
40
DRDO CSE 2022 Paper 2 | Question: 32
A perceptron consists of weights $\left[w_{1}, w_{2}, w_{3}, w_{4}\right]=[0.5,2,1,-3]$. The activation function is provided as $y=f(z)=1$ if $z \geq 2$ otherwise $0,$ where $z= \sum(w . d)$. What is the output $y$ ...
Kazuha
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in
Artificial Intelligence
Oct 3, 2023
by
Kazuha
392
views
drdocse-2022-paper2
artificial-intelligence
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