AKTU Application of Soft Computing · Important Questions 2026Source: ryzenstudy.com PYQs
BCS056 Application of Soft Computing Important Questions 2026 (AKTU)
Repeat analysis + most-asked questions, unit-wise
What we did: built from all 5 AKTU Application of Soft Computing papers (2021-22–2025-26): 119 questions grouped into 64 distinct questions, 34 of which were asked in more than one paper.
- of marks were repeat questions
- 58%
- of marks were repeat questions
- questions asked in 2+ papers
- 34
- questions asked in 2+ papers
- papers analysed (2021-22–2025-26)
- 5
- papers analysed (2021-22–2025-26)
- units, mapped to current syllabus
- 5
- units, mapped to current syllabus
How much of the paper repeats?
- Repeats are real: when we checked each newer BCS056 paper against the papers before it, on average 58% of its marks were questions already asked in an earlier paper.
- 2023-24 paper: 43% of marks were repeats (7 of 11 repeated questions were in our top list).
- 2024-25 paper: 62% of marks were repeats (4 of 13 repeated questions were in our top list).
- 2025-26 paper: 68% of marks were repeats (5 of 15 repeated questions were in our top list).
- Topic weightage is a guide, not a guarantee: our Tier 1 + Tier 2 topics covered 53% of the next paper's marks — picking the same number of topics at random would have covered about 50%.
Across 10 subjects and 23 papers we tested, 43% of marks repeated earlier questions on average.
1.Unit-wise most-asked questions
Ranked by how many papers asked them, then by how recently. Click a paper to open it.
Unit 1:
21% of past marks| Question | Asked |
|---|---|
| Explain various learning techniques in neural networks and analyze their suitability for supervised and unsupervised problems. | Asked 3× |
| Define an artificial neural network. | Asked 3× |
| Critically analyze different activation functions and justify why sigmoid or ReLU is preferred in deep neural networks. | Asked 2× |
| How does bias improve the learning capability of an artificial neuron? | Asked 2× |
Unit 2:
21% of past marks| Question | Asked |
|---|---|
| Why does a single-layer perceptron fail to solve linearly non-separable problems? | Asked 3× |
| Compare backpropagation learning with perceptron learning in terms of error handling, learning capability, and convergence. | Asked 2× |
| Evaluate the suitability of backpropagation neural networks for pattern recognition applications. | Asked 2× |
| Discuss the effect of learning rate and momentum coefficient on stability and convergence of backpropagation training. | Asked 2× |
Unit 3: Fuzzy Logic-I (Introduction)
17% of past marks| Question | Asked |
|---|---|
| Evaluate the importance of properties of fuzzy sets (normality, convexity, support) in practical fuzzy systems. | Asked 3× |
| Explain the role of fuzzy relations in decision-making with examples. | Asked 3× |
| How does a fuzzy set differ from a crisp set in terms of membership function? | Asked 2× |
| Provide a detailed analysis of fuzzy logic's importance in artificial intelligence. | Asked 2× |
Unit 4:
20% of past marks| Question | Asked |
|---|---|
| Apply fuzzy algorithms to handle uncertainty in industrial decision-making systems. | Asked 2× |
| Explain the concept of fuzzy implications. | Asked 2× |
| Analyze the role of fuzzy controllers in industrial automation with suitable examples. | Asked 2× |
| Define the membership function and state its importance in fuzzy logic. Also discuss the features of membership functions. | Asked 2× |
Unit 5: Genetic Algorithm(GA)
21% of past marks| Question | Asked |
|---|---|
| State any two genetic operators used in Genetic Algorithm. | Asked 4× |
| Explain crossover and mutation operators and their role in GA. | Asked 3× |
| Explain the working principle of Genetic Algorithm with a neat flow chart. | Asked 3× |
| Provide a detailed explanation of the industrial applications of genetic algorithms. | Asked 3× |
2.Topic weightage: which topics carry the marks
Blue = Tier 1 + Tier 2 (topics that carried ~80% of past marks). Grey = the rest. It shows where marks have gone, not a promise of what comes next.
Properties of fuzzy sets · U3
5.8% (cum 6%)
Multilayer perceptron · U2
5.4% (cum 11%)
Perceptron model · U2
4.8% (cum 16%)
Artificial neuron model · U1
4.4% (cum 20%)
Factors and limitations of backprop · U2
4.3% (cum 25%)
Fuzzy controller · U4
3.8% (cum 29%)
Learning techniques · U1
3.8% (cum 32%)
Network architectures · U1
3.6% (cum 36%)
Associative memory · U1
3.5% (cum 39%)
Crossover · U5
3.4% (cum 43%)
Basic concepts of fuzzy logic · U3
3.3% (cum 46%)
GA procedure and flow chart · U5
3.3% (cum 49%)
Fuzzy inference · U4
3.2% (cum 53%)
Fuzzification · U4
2.9% (cum 55%)
Fuzzy implications and algorithms · U4
2.8% (cum 58%)
Learning rule coefficient · U2
2.8% (cum 61%)
Fuzzy and crisp relations · U3
2.8% (cum 64%)
Fitness function · U5
2.7% (cum 67%)
Genetic operators · U5
2.6% (cum 69%)
Defuzzification · U4
2.6% (cum 72%)
Initialization and selection · U5
2.6% (cum 74%)
Neuron structure and synapse · U1
2.5% (cum 77%)
Activation functions · U1
2.5% (cum 79%)
Fuzzy if-then rules · U4
2.2% (cum 82%)
GA applications · U5
2.2% (cum 84%)
Fuzzy set operations · U3
2.1% (cum 86%)
Backpropagation algorithm · U2
2.1% (cum 88%)
Fuzzy vs crisp sets · U3
1.9% (cum 90%)
Backpropagation applications · U2
1.8% (cum 92%)
Basic concepts and working principle · U5
1.8% (cum 93%)
Membership functions · U4
1.5% (cum 95%)
Industrial applications · U4
1.1% (cum 96%)
Mutation · U5
1.1% (cum 97%)
Perceptron and convergence rule · U1
1% (cum 98%)
Fuzzy to crisp conversion · U3
0.8% (cum 99%)
Generational cycle · U5
0.8% (cum 100%)
Genetic encoding · U5
0.4% (cum 100%)
| Topic | Papers | Share |
|---|---|---|
| Properties of fuzzy sets · Unit 3 · Tier 1 | 3/5 | 5.8% |
| Multilayer perceptron · Unit 2 · Tier 1 | 4/5 | 5.4% |
| Perceptron model · Unit 2 · Tier 1 | 4/5 | 4.8% |
| Artificial neuron model · Unit 1 · Tier 1 | 4/5 | 4.4% |
| Factors and limitations of backprop · Unit 2 · Tier 1 | 3/5 | 4.3% |
| Fuzzy controller · Unit 4 · Tier 1 | 4/5 | 3.8% |
| Learning techniques · Unit 1 · Tier 1 | 3/5 | 3.8% |
| Network architectures · Unit 1 · Tier 1 | 2/5 | 3.6% |
| Associative memory · Unit 1 · Tier 1 | 3/5 | 3.5% |
| Crossover · Unit 5 · Tier 1 | 3/5 | 3.4% |
| Basic concepts of fuzzy logic · Unit 3 · Tier 1 | 3/5 | 3.3% |
| GA procedure and flow chart · Unit 5 · Tier 1 | 3/5 | 3.3% |
| Fuzzy inference · Unit 4 · Tier 1 | 3/5 | 3.2% |
| Fuzzification · Unit 4 · Tier 2 | 3/5 | 2.9% |
| Fuzzy implications and algorithms · Unit 4 · Tier 2 | 3/5 | 2.8% |
| Learning rule coefficient · Unit 2 · Tier 2 | 3/5 | 2.8% |
| Fuzzy and crisp relations · Unit 3 · Tier 2 | 3/5 | 2.8% |
| Fitness function · Unit 5 · Tier 2 | 2/5 | 2.7% |
| Genetic operators · Unit 5 · Tier 2 | 4/5 | 2.6% |
| Defuzzification · Unit 4 · Tier 2 | 2/5 | 2.6% |
| Initialization and selection · Unit 5 · Tier 2 | 2/5 | 2.6% |
| Neuron structure and synapse · Unit 1 · Tier 2 | 3/5 | 2.5% |
| Activation functions · Unit 1 · Tier 2 | 2/5 | 2.5% |
| Fuzzy if-then rules · Unit 4 · Tier 2 | 1/5 | 2.2% |
| GA applications · Unit 5 · Tier 3 | 3/5 | 2.2% |
| Fuzzy set operations · Unit 3 · Tier 3 | 3/5 | 2.1% |
| Backpropagation algorithm · Unit 2 · Tier 3 | 2/5 | 2.1% |
| Fuzzy vs crisp sets · Unit 3 · Tier 3 | 3/5 | 1.9% |
| Backpropagation applications · Unit 2 · Tier 3 | 2/5 | 1.8% |
| Basic concepts and working principle · Unit 5 · Tier 3 | 2/5 | 1.8% |
| Membership functions · Unit 4 · Tier 3 | 2/5 | 1.5% |
| Industrial applications · Unit 4 · Tier 3 | 1/5 | 1.1% |
| Mutation · Unit 5 · Tier 3 | 1/5 | 1.1% |
| Perceptron and convergence rule · Unit 1 · Tier 3 | 2/5 | 1% |
| Fuzzy to crisp conversion · Unit 3 · Tier 3 | 1/5 | 0.8% |
| Generational cycle · Unit 5 · Tier 3 | 1/5 | 0.8% |
| Genetic encoding · Unit 5 · Tier 3 | 1/5 | 0.4% |
3.Paper pattern (2025-26)
| Section | Questions | Rule |
|---|---|---|
| A | 7 × 2 marks | Attempt all |
| B | 5 × 7 marks | Attempt any 3 |
| C | 11 × 7 marks | Attempt any 1 |
4.In the syllabus, never asked yet
Low priority, but read them once — a new paper can always start here.
- Single layer ANN · U2
5.Study order
- 1Properties of fuzzy sets (Unit 3, Tier 1)
- 2Multilayer perceptron (Unit 2, Tier 1)
- 3Perceptron model (Unit 2, Tier 1)
- 4Artificial neuron model (Unit 1, Tier 1)
- 5Factors and limitations of backprop (Unit 2, Tier 1)
- 6Fuzzy controller (Unit 4, Tier 1)
- 7Learning techniques (Unit 1, Tier 1)
- 8Network architectures (Unit 1, Tier 1)
- 9Associative memory (Unit 1, Tier 1)
- 10Crossover (Unit 5, Tier 1)
- 11Basic concepts of fuzzy logic (Unit 3, Tier 1)
- 12GA procedure and flow chart (Unit 5, Tier 1)
- 13Fuzzy inference (Unit 4, Tier 1)
- 14Fuzzification (Unit 4, Tier 2)
- 15Fuzzy implications and algorithms (Unit 4, Tier 2)
- 16Learning rule coefficient (Unit 2, Tier 2)
- 17Fuzzy and crisp relations (Unit 3, Tier 2)
- 18Fitness function (Unit 5, Tier 2)
- 19Genetic operators (Unit 5, Tier 2)
- 20Defuzzification (Unit 4, Tier 2)
- 21Initialization and selection (Unit 5, Tier 2)
- 22Neuron structure and synapse (Unit 1, Tier 2)
- 23Activation functions (Unit 1, Tier 2)
- 24Fuzzy if-then rules (Unit 4, Tier 2)
FAQ
Which units of BCS056 carry the most marks?
Unit 1 (, 21% of past marks) and Unit 2 (, 21% of past marks).
Do AKTU Application of Soft Computing questions repeat?
Yes. On average 58% of the marks in each newer BCS056 paper came from questions already asked in an earlier paper, and 34 questions have appeared in more than one paper.
How is this list made?
Every question from the 5 papers (2021-22–2025-26) was mapped to the current AKTU syllabus and grouped with the same question from other years. Questions are ranked by how many papers they appeared in, then by how recently.
Study this subject
Note: this is based on past AKTU papers, not a guarantee. Questions are mapped to the current syllabus; topics that are no longer in the syllabus are left out.