{ "schema": 1, "subject": "is2", "current_syllabus_year": "2026-27", "unit_count": 4, "scope_note": "Every recovered distinct paper is mapped. Same-date programme/batch variants remain separate; duplicate archive copies do not count as new papers or recurrence. Outside-current-syllabus parts are recorded but excluded from preparation totals.", "papers": [ { "id": "is2-2026-01-31", "date": "2026-01-31", "exam_type": "reexam", "academic_year": "2025-26", "chapter_id": "paper-reexam-2026", "source_course_code": "702AI0C019", "source_file": "QP_ReExam_2025-26.pdf", "source_sha256": "20cd22bb86495f056f61bf53d68d20e3909fee0afb08ea4fe45dab2c3b7959ce", "source_pages": 6, "source_course_code_origin": "archive filename; not printed in the paper header", "visual_review": { "status": "reviewed", "receipt": "audit/cn_is2_final_20261014.md", "note": "Reuses unchanged original-page review, not a claim of a new viewing." }, "pattern": { "time_minutes": 180, "offered_marks": 140, "attempted_marks": 100, "compulsory_questions": [ "Q1" ], "choose": 4, "from": [ "Q2", "Q3", "Q4", "Q5", "Q6", "Q7" ], "whole_question_marks": 20 }, "questions": [ { "id": "is2-2026-01-31-q1a", "label": "Q1(a)", "marks": 5, "marks_status": "printed, visually checked", "unit": 1, "topic": "Joint probability: disease, fever and test", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 1 ], "internal_tasks": [ { "label": "1", "task": "Disease given fever and positive test", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "Overall positive-test probability", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "Disease given no fever", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. For a disease Y with fever X as a symptom, a test Z is conducted. Joint probabilities: Disease (Y=1): fever & test+ 0.576, fever & test− 0.144, no fever & test+ 0.072, no fever & test− 0.016; No disease (Y=0): 0.008, 0.064, 0.012, 0.108. (1) If fever occurs and the test is positive, what is the probability of disease? (2) What is the probability that a random person tests positive? (3) Given no fever, what is the probability of still having the disease?", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q1a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "da0cf0aae5cdca925de450015aade3da3539ac0fd74527c874b5683366deb96d", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q1b", "label": "Q1(b)", "marks": 5, "marks_status": "printed, visually checked", "unit": 3, "topic": "PSO applications", "type": "theory", "scope": "current-syllabus", "source_pages": [ 1 ], "internal_tasks": [], "prompt": "List the applications of Particle Swarm Optimization and explain any one application in detail.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q1b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "f11e913bae61a6a01619c3aa4aeddf578adc10f10fcbab43b7077a7c6b99cdbf", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q1c", "label": "Q1(c)", "marks": 5, "marks_status": "printed, visually checked", "unit": 2, "topic": "Fuzzy set operations", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 1 ], "internal_tasks": [ { "label": "1", "task": "Union", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "Intersection", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "Union of complements", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "4", "task": "Intersection of complements", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. X = {(c1, 0.7), (c2, 0.9), (c3, 0.2), (c4, 0.4), (c5, 0.9)}, Y = {(c1, 0.6), (c2, 0.3), (c3, 0.8), (c4, 0.7), (c5, 0.1)}. Find X ∪ Y, X ∩ Y, ¬X ∪ ¬Y, ¬X ∩ ¬Y.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q1c", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "653e378e8fb5e05dd8894f305870cc39dd26d03600f691521f29539891f8f704", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q1d", "label": "Q1(d)", "marks": 5, "marks_status": "printed, visually checked", "unit": 4, "topic": "Two-state Markov bank model", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 2 ], "internal_tasks": [ { "label": "a", "task": "Transition matrix", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "b", "task": "State diagram", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. Each year 20% of Savings holders switch to Current and 80% stay; 10% of Current holders switch to Savings and 90% stay. (a) Construct the transition matrix. (b) Draw the state diagram.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q1d", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "08aaab30b7bd44d348b218c8d4896d42d461ad0c9fc2ad7afb773fb0809c67a4", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q2a", "label": "Q2(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 2, "topic": "Mamdani fire-controller design and centroid", "type": "case", "scope": "current-syllabus", "source_pages": [ 2 ], "internal_tasks": [ { "label": "a", "task": "Triangular membership functions", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "b", "task": "At least nine rules", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "c", "task": "Mamdani inference and defuzzification", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "scenario", "task": "Temperature 65 and smoke 80: predict fire risk", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "A building safety department wants a fuzzy logic system to predict fire hazards. Inputs are Temperature (1–100°C) and Smoke Level (1–100). The output Fire Risk (1–100) has labels Low, Moderate and High. Define triangular membership functions, construct at least nine rules, explain Mamdani inference and defuzzify for Temperature=65°C and Smoke=80.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q2a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "3921cfa2c1c59e466a24e2c816b139ffb911bd8a87d84908c90cbbefa732aab0", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q2b", "label": "Q2(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 1, "topic": "GMM, k-means, likelihood and parameters", "type": "mixed", "scope": "current-syllabus", "source_pages": [ 2 ], "internal_tasks": [ { "label": "bullet 1", "task": "GMM versus k-means", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "bullet 2", "task": "Derive log likelihood", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "bullet 3", "task": "Parameter count for K=5", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "With reference to the Gaussian Mixture Model (GMM): compare GMM with the k-means algorithm; derive the equation for the log-likelihood function; state the number of parameters to be computed if K = 5.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q2b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "097158a93a2644f5a7f2d46de2fb17f5eb1277060ada624be69aad32597a4e2c", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q3a", "label": "Q3(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 1, "topic": "Bayesian network: fever given no flu", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 2, 3 ], "internal_tasks": [], "prompt": "Question. Weather → Flu, Weather → Cough, Flu and Cough → Fever. P(Weather) = 0.75; P(Flu | W) = 0.2, P(Flu | ¬W) = 0.5; P(Cough | W) = 0.70, P(Cough | ¬W) = 0.25; P(Fever | Flu, Cough) = 0.60, P(Fever | Flu, ¬Cough) = 0.02, P(Fever | ¬Flu, Cough) = 0.60, P(Fever | ¬Flu, ¬Cough) = 0.01. Calculate the probability of a person having fever given he doesn't have flu.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q3a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "832c32edf7f8adc19e5fad1e0cbffd4e33a9814b48180eac68d5263cdcfdeba2", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q3b", "label": "Q3(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 2, "topic": "Fuzzy Cartesian product and composition", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 3 ], "internal_tasks": [ { "label": "1", "task": "R = A Cartesian-product B", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "S = C Cartesian-product B", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "Max-min composition S after R", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. A = {0.6/small + 0.4/medium + 0.1/large}, B = {0.4/slow + 0.8/moderate + 0.3/fast}, C = {0.5/small + 0.2/medium + 0.4/large}. Find R = A × B, S = C × B and the max–min composition S ∘ R.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q3b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "124264f59dae3b068f562265cf5ba4cf11b62011ffe03f04193b6fad644c2289", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q4a", "label": "Q4(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 3, "topic": "PSO one iteration and best memories", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 3 ], "internal_tasks": [], "prompt": "Question. Maximize F(x)=x_1^2-x_1x_2+x_2^2+3x_1+4x_2+5 using Particle Swarm Optimization. X=\\begin{bmatrix}3.1472&-4.0246\\\\4.0579&-2.2150\\end{bmatrix},\\;V=\\begin{bmatrix}0.2194&0.2449\\\\0.1908&0.2228\\end{bmatrix} . Take W=0.9,\\;c_1=c_2=1.5,\\;r_1=0.5949,\\;r_2=0.0855 . Optimize and give the solution after 1 iteration showing the step-by-step process.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q4a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "08c8ab06ddb587c3aebaf77ef44e4f3670f79818d82a5c27478b69d31b5e8e42", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q4b", "label": "Q4(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 1, "topic": "Naive Bayes job classification", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 4 ], "internal_tasks": [], "prompt": "Question. Predict whether a candidate accepts a job offer from Experience (Fresher/Mid/Senior), Expected Salary (Low/Medium/High) and Company Reputation (Good/Average). Data: (Fresher, Low, Good, Yes), (Mid, High, Good, Yes), (Senior, High, Average, No), (Fresher, Medium, Average, Yes), (Senior, Low, Good, No), (Mid, Medium, Good, Yes), (Fresher, High, Average, No), (Senior, Medium, Average, No). New candidate: Mid, Low, Good.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q4b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "0ca6bd23a2cbc60a22e28a88449f788dcb730eeba6887a168182fb93bfc3e085", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q5a", "label": "Q5(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 3, "topic": "Genetic algorithm offspring and fitness", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 4 ], "internal_tasks": [], "prompt": "Implement a GA to maximise f(x)=2x^3-3x^2+8x for x in [0,50]. Take 4-bit representations of 12, 20, 17, 13. Crossover after the second bit and mutate by flipping any one bit. Produce two offspring and comment on fitness.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q5a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "767c042a307cc2f58ef9827bd8d1e87317a31b8917313a1e33a957931d7fd4dd", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q5b", "label": "Q5(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 4, "topic": "Markov booking channels and stationary distribution", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 4, 5 ], "internal_tasks": [ { "label": "1", "task": "Transition matrix", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "Retention and movement interpretation", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "State-transition diagram", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "4", "task": "Next distribution", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "5", "task": "Long-term behaviour", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. Bookings come via Website (W), App (A) or Counter (C). From W: 50% W, 30% A, 20% C. From A: 40% A, 40% W, 20% C. From C: 20% W, 30% A, 50% C. 1. Develop the transition matrix. 2. Interpret retention and gain/loss. 3. Draw the diagram. 4. If now 40% W, 35% A, 25% C, estimate next week. 5. Which channel grows or shrinks in the long term?", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q5b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "75d31fef94035faba78fc0e30bf4d50c3303aebd7e2b0c08148a67e0e5bf828b", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q6a", "label": "Q6(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 3, "topic": "ACO travelling salesman tour and pheromones", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 5 ], "internal_tasks": [], "prompt": "Question. For the four-city travelling salesman problem the distance matrix and pheromone matrix are given (all pheromones 1). Apply Ant Colony Optimization. Calculate the path travelled by 2 ants starting from city A; random numbers for Ant 1 are 0.3, 0.8 and for Ant 2 are 0.5, 0.7. Also update the pheromone matrix after 1 iteration.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q6a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "bcd001b48d14113d1532411408741ef37eb18e0fe4bfe4320b90659e1e86d8f9", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q6b", "label": "Q6(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 4, "topic": "Markov aircraft movement and stationary distribution", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 5, 6 ], "internal_tasks": [ { "label": "1", "task": "Transition matrix", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "Retention and movement interpretation", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "State-transition diagram", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "4", "task": "Next distribution", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "5", "task": "Long-term behaviour", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. Planes at A: 50% stay, 30% to B, 20% to C. At B: 40% to A, 40% stay, 20% to C. At C: 25% to A, 25% to B, 50% stay. 1. Construct the matrix. 2. Interpret retention and movement. 3. Draw the diagram. 4. For [30, 40, 30] find the next night's distribution. 5. Which airport is a long-term net gainer or loser?", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q6b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "bb67bf83b75c811d9d238a2a060b1a97ee2d0d0219394ab01a46468db14d2471", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q7a", "label": "Q7(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 2, "topic": "Fuzzy logic applications", "type": "theory", "scope": "current-syllabus", "source_pages": [ 6 ], "internal_tasks": [], "prompt": "List the applications of fuzzy logic. Explain any one application in detail.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q7a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "0c7ed09349c4a5c44b534ae3dc6ab6ec0cf88b00c12f0d7ea600a825cc7bdc74", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2026-01-31-q7b", "label": "Q7(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 4, "topic": "Bellman equation and RL applications", "type": "theory", "scope": "current-syllabus", "source_pages": [ 6 ], "internal_tasks": [], "prompt": "Explain the Bellman equation in reinforcement learning. Also give applications of reinforcement learning.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-reexam-2026/q7b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "1cd5b2970f02f02bbb867bff7f5b99486ab707105ead3d19d1e56be53a7db056", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." } ] }, { "id": "is2-2025-11-24", "date": "2025-11-24", "exam_type": "final", "academic_year": "2025-26", "chapter_id": "paper-final-2025", "source_course_code": "702AI0C019", "source_file": "QP_Final_2025-26.pdf", "source_sha256": "f56a55a1b212dfb94276fa3e656d32437479a0be40ce032d9422f41432111190", "source_pages": 6, "source_course_code_origin": "archive filename; not printed in the paper header", "visual_review": { "status": "reviewed", "receipt": "audit/cn_is2_final_20261014.md", "note": "Reuses unchanged original-page review, not a claim of a new viewing." }, "pattern": { "time_minutes": 180, "offered_marks": 140, "attempted_marks": 100, "compulsory_questions": [ "Q1" ], "choose": 4, "from": [ "Q2", "Q3", "Q4", "Q5", "Q6", "Q7" ], "whole_question_marks": 20 }, "questions": [ { "id": "is2-2025-11-24-q1a", "label": "Q1(a)", "marks": 5, "marks_status": "printed, visually checked", "unit": 1, "topic": "Joint probability and conditional inference", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 1 ], "internal_tasks": [ { "label": "a.1", "task": "Female given premium", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "a.2", "task": "Credit card given premium", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "b.1", "task": "Overall premium probability", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "b.2", "task": "Overall credit-card probability", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "c", "task": "Regular given male and debit card", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. A supermarket recorded the joint distribution of Product type (Y: premium = 1, regular = 0), Payment mode (X: credit = 1, debit = 0) and Gender (Z: female = 1, male = 0). (a) If a customer bought a premium item, find the probability that (1) they are female, (2) they paid by credit card. (b) Find (1) P(premium) and (2) P(credit card). (c) Given a male customer paid by debit card, find the probability he bought a regular item.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q1a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "53bb2d94aa9894a1d35ba4bc0d78c57e23897d0869cb1b9cd3e055d839a61e2d", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q1b", "label": "Q1(b)", "marks": 5, "marks_status": "printed, visually checked", "unit": 3, "topic": "ACO applications", "type": "theory", "scope": "current-syllabus", "source_pages": [ 1 ], "internal_tasks": [], "prompt": "List the applications of Ant Colony Optimization and explain any one application in detail.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q1b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "2e4303ebf3d978769df4113fef20843f69604ab078465288eb018dc190f141e8", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q1c", "label": "Q1(c)", "marks": 5, "marks_status": "printed, visually checked", "unit": 2, "topic": "Fuzzy set operations", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 2 ], "internal_tasks": [ { "label": "1", "task": "Union", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "Intersection", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "Union of complements", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "4", "task": "Intersection of complements", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. Consider two fuzzy sets X = {(c1, 0.6), (c2, 0.8), (c3, 0.3), (c4, 0.5), (c5, 0.7)} and Y = {(c1, 0.4), (c2, 0.5), (c3, 0.9), (c4, 0.6), (c5, 0.2)}. Find 1) X ∪ Y 2) X ∩ Y 3) ¬X ∪ ¬Y 4) ¬X ∩ ¬Y.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q1c", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "266e705cd0a014d7b61f71361ebe7a644c97aa379831fafd0fab8af1cdb773cb", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q1d", "label": "Q1(d)", "marks": 5, "marks_status": "printed, visually checked", "unit": 4, "topic": "Two-state Markov transition model", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 2 ], "internal_tasks": [ { "label": "a", "task": "Transition matrix", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "b", "task": "State diagram", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. Users switch between Brand X and Brand Y each year: 25% of X users switch to Y and 75% stay; 40% of Y users switch to X and 60% stay. (a) Write the transition probability matrix. (b) Draw the state transition diagram.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q1d", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "972235f7198000ce22adfb8b641ab559bd021a1b94c842d963b0bc926b6a14e8", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q2a", "label": "Q2(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 3, "topic": "PSO one iteration and best memories", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 2 ], "internal_tasks": [], "prompt": "Question. Maximize F(x_1,x_2)=2x_1^2-3x_1x_2+x_2^2+5x_1+2x_2+7 using Particle Swarm Optimization. X=\\begin{bmatrix}2.5&-3.0\\\\3.5&-1.5\\end{bmatrix},\\;V=\\begin{bmatrix}0.2&0.3\\\\0.1&0.25\\end{bmatrix} . Take W=0.9,\\;c_1=c_2=1.5,\\;r_1=0.5949,\\;r_2=0.0855 . Optimize and give the solution after 1 iteration showing the step-by-step process.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q2a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "c13cdda2f3dff30679e86852691263db9817af7e08840ca8773757835be6ce37", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q2b", "label": "Q2(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 1, "topic": "GMM, k-means, likelihood and parameters", "type": "mixed", "scope": "current-syllabus", "source_pages": [ 2 ], "internal_tasks": [ { "label": "bullet 1", "task": "GMM versus k-means", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "bullet 2", "task": "Derive log likelihood", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "bullet 3", "task": "Parameter count for K=5", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "With reference to the Gaussian Mixture Model (GMM): compare GMM with the k-means algorithm; derive the equation for the log-likelihood function; state the number of parameters to be computed if K = 5.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q2b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "097158a93a2644f5a7f2d46de2fb17f5eb1277060ada624be69aad32597a4e2c", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q3a", "label": "Q3(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 1, "topic": "Bayesian network: wet grass given no rain", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 3 ], "internal_tasks": [], "prompt": "Question. Cloudy → Sprinkler, Cloudy → Rain, Sprinkler and Rain → Wet Grass. P(Cloudy) = 0.5; P(Sprinkler | C) = 0.10, P(Sprinkler | ¬C) = 0.50; P(Rain | C) = 0.80, P(Rain | ¬C) = 0.20; P(Wet | S, R) = 0.99, P(Wet | S, ¬R) = 0.90, P(Wet | ¬S, R) = 0.90, P(Wet | ¬S, ¬R) = 0.01. Calculate the probability of the grass being wet given it doesn't rain.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q3a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "5c76ecdd2c647d6c5b4054e0b99f963263ee106a89a04763c1c9c54693e46d6a", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q3b", "label": "Q3(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 2, "topic": "Mamdani loan-controller design and centroid", "type": "case", "scope": "current-syllabus", "source_pages": [ 3 ], "internal_tasks": [ { "label": "a", "task": "Triangular membership functions", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "b", "task": "Nine-rule base", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "c", "task": "Mamdani inference and defuzzification", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "d", "task": "Credit 55, stability 35: output and interpretation", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "A bank uses fuzzy decision support with Credit Score and Income Stability, both on 1–100. Loan Approval is a score on 1–100 with labels Reject, Consider and Approve. Define triangular membership functions, construct nine rules, explain Mamdani inference and defuzzify for Credit Score=55 and Income Stability=35.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q3b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "7a8373af18b4c3a8a94601e661a2c67d8de3afa5a9dbdae098422b15bf9884e9", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q4a", "label": "Q4(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 2, "topic": "Fuzzy Cartesian product and composition", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 3, 4 ], "internal_tasks": [ { "label": "1", "task": "R = A Cartesian-product B", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "S = C Cartesian-product B", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "Max-min composition S after R", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. Consider fuzzy sets A = {0.4/small + 0.3/medium + 0.1/large} and B = {0.2/slow + 0.7/moderate + 0.3/fast}. 1. Find R = A × B. 2. Given C = {0.3/small + 0.1/medium + 0.4/large}, find S = C × B. 3. Calculate the max–min composition S ∘ R.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q4a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "60af0d6f51afa0ce3263780bf8d19088b889e1ea6978bd1826b8347663ca79a5", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q4b", "label": "Q4(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 1, "topic": "Naive Bayes flu classification", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 4 ], "internal_tasks": [], "prompt": "Question. A hospital wants to predict flu from Fever (Yes/No), Cough (Yes/No) and Temperature Level (Low/Normal/High). Training data: (Yes, Yes, High, Yes), (No, No, Normal, No), (Yes, No, Low, No), (Yes, Yes, High, Yes), (No, Yes, Normal, No), (Yes, Yes, High, Yes), (No, No, Low, No), (Yes, No, Normal, No). Using naive Bayes, predict whether a patient with Fever = Yes, Cough = Yes, Temperature = Normal has flu.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q4b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "fb1d829ace95e2f43268eef4cea10234c289bb034c76cf62ec250946856bc10c", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q5a", "label": "Q5(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 4, "topic": "Markov hotel movement and stationary distribution", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 4, 5 ], "internal_tasks": [ { "label": "1", "task": "Transition matrix", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "Retention and movement interpretation", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "State-transition diagram", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "4", "task": "Next distribution", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "5", "task": "Long-term behaviour", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. A hotel chain has properties in Paris (P), London (L) and Rome (R). Of guests in Paris, 60% stay, 25% go to London, 15% to Rome; in London 30% go to Paris, 50% stay, 20% go to Rome; in Rome 20% go to Paris, 30% to London, 50% stay. 1. Construct the transition matrix. 2. Explain the diagonal and off-diagonal entries. 3. Draw the state diagram. 4. Today's counts are [50, 30, 20]; find tomorrow's expected counts. 5. Which city gains or loses guests in the long run?", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q5a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "9b1701e52863910f88f6cd37cea9bb44413419ff35971b974e52ce7f2340c6df", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q5b", "label": "Q5(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 3, "topic": "Genetic algorithm offspring and fitness", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 5 ], "internal_tasks": [], "prompt": "Implement a GA to maximise f(x)=2x^3-5x^2+7x for x in [0,15]. Take 4-bit representations of 13, 22, 18, 23. Crossover after the second bit and mutate by flipping any one bit. Produce two offspring and comment on fitness.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q5b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "d865aae946d0c8ded2ec730332744a097e8aaadf86228db3d9d1503fd501b99d", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q6a", "label": "Q6(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 2, "topic": "Fuzzy logic applications", "type": "theory", "scope": "current-syllabus", "source_pages": [ 5 ], "internal_tasks": [], "prompt": "List the applications of fuzzy logic. Explain any one application in detail.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q6a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "0c7ed09349c4a5c44b534ae3dc6ab6ec0cf88b00c12f0d7ea600a825cc7bdc74", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q6b", "label": "Q6(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 4, "topic": "Markov learner progression and stationary state", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 5, 6 ], "internal_tasks": [ { "label": "1", "task": "Transition matrix", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "2", "task": "Retention and movement interpretation", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "3", "task": "State-transition diagram", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "4", "task": "Next distribution", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" }, { "label": "5", "task": "Long-term behaviour", "marks": null, "marks_status": "not separately printed", "solution_coverage": "complete" } ], "prompt": "Question. Students are Beginner (B), Intermediate (I) or Advanced (A). Of Beginners, 60% remain, 30% become Intermediate, 10% go to Advanced. Of Intermediates, 20% fall back, 50% remain, 30% progress. Of Advanced, 10% drop to Intermediate, 80% remain and 10% stop (treated as remaining in A). 1. Construct the matrix. 2. Interpret retention, progression and regression. 3. Draw the diagram. 4. For [50, 30, 20], find the distribution after one day. 5. Which level has the largest steady-state share?", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q6b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "2c1dc2b5e9d883214fc80e344194daa6470608390ef545251960ae04bdeddda6", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q7a", "label": "Q7(a)", "marks": 10, "marks_status": "printed, visually checked", "unit": 3, "topic": "PSO applications", "type": "theory", "scope": "current-syllabus", "source_pages": [ 6 ], "internal_tasks": [], "prompt": "List the applications of Particle Swarm Optimization and explain any one application in detail.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q7a", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "061031a224875eb9d7e862019c029d4f6bcbcb284c63e642a2704a6df87915b2", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." }, { "id": "is2-2025-11-24-q7b", "label": "Q7(b)", "marks": 10, "marks_status": "printed, visually checked", "unit": 3, "topic": "ACO travelling salesman tour and pheromones", "type": "numerical", "scope": "current-syllabus", "source_pages": [ 6 ], "internal_tasks": [], "prompt": "Question. For the four-city travelling salesman problem, the distance and pheromone matrices (A, B, C, D) are given. Apply Ant Colony Optimization. Calculate the path travelled by 2 ants to cover all cities starting from city A. Random numbers for Ant 1 are 0.3, 0.8 and for Ant 2 are 0.5, 0.7. Also update the pheromone matrix after 1 iteration.", "prompt_status": "student-facing transcription, checked against original paper", "answer_route": "/is2/#tee/papers/paper-final-2025/q7b", "solution_coverage": "complete", "solution_review": "audit/cn_is2_final_20261014.md", "answer_html_sha256": "f706a7ced8b68040ad3af150f8807c83ec263c1cd78b534096b5afd6435f8eae", "subpart_marks_note": "Printed internal allocations retained where present; otherwise marks belong to the whole lettered part and are not split." } ] }, { "source_id": "is2-15", "date": "2025-04-28", "exam_type": "final-and-reexam", "academic_year": "2024-25 / reexam 2023-24", "chapter_id": "paper-2025-april", "title": "Final / re-examination · 28 Apr 2025", "id": "is2-2025-04-28", "batch": "Historical Semester VI", "source_file": "Intelligent Systems -II, Sem VI, Final Exam 2024-25 & Re Exam 2023-24.pdf", "source_sha256": "f472be862d2d78c06cab82d50e939ee676ffb7f2e97707662cf13b151eaa9f51", "source_pages": 6, "visual_review": { "status": "reviewed", "receipt": "audit/is2_older_papers_20261014.md", "note": "All six pages newly visually inspected; table values, diagrams, marks and continuations transcribed." }, "pattern": { "time_minutes": 180, "offered_marks": 140, "attempted_marks": 100, "compulsory_questions": [ "Q1" ], "choose": 4, "from": [ "Q2", "Q3", "Q4", "Q5", "Q6", "Q7" ], "whole_question_marks": 20 }, "questions": [ { "label": "Q1(a)", "marks": 5, "source_pages": [ 1 ], "unit": 1, "topic": "Joint masses, normalization and conditional probability", "prompt": "Machine No Fault (X = 0) Fault (X = 1) Machine A 0.40 0.05 Machine B 0.25 0.10 Machine C 0.20 0.08 Machine D 0.05 0.07 A factory uses four machines (Machine A, Machine B, Machine C, and Machine D) for production. The above table shows the joint probability distribution of a fault occurring (X) and the machine type (Y). If a fault occurs, what is the probability that it came from Machine C? What is the probability that a randomly selected product has a fault? Given the product is from Machine B, what is the probability that it has a fault?", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "Printed joint table totals1.20. Conditional ratios valid under common mass normalization; unconditional answer0.25 explicitly conditional on that repair. Raw0.30 preserved as mass, not silently accepted probability.", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q1a", "answer_route": "/is2/#tee/papers/paper-2025-april/q1a", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "628a2f762ea57f68144a8df0e5b8fece23b42d31424e1c507bcc940883f319bc" }, { "label": "Q1(b)", "marks": 5, "source_pages": [ 1 ], "unit": 3, "topic": "Genetic algorithm applications", "prompt": "List the applications of genetic algorithm and explain any one application in detail.", "type": "theory", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q1b", "answer_route": "/is2/#tee/papers/paper-2025-april/q1b", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "2ea0fedc2b9445ecc5951005aaeaf596e1d84062fc8a40675d633921ed59d1dd" }, { "label": "Q1(c)", "marks": 5, "source_pages": [ 2 ], "unit": 2, "topic": "Fuzzy union, intersection and complements", "prompt": "Consider two fuzzy sets X={(c1,0.6),(c2,0.8),(c3,0.3),(c4,0.5),(c5,0.7)} Y={(c1,0.4),(c2,0.5),(c3,0.9),(c4,0.6),(c5,0.2)} Find 1) X∪Y 2) X∩Y 3) ¬X∪¬Y 4) ¬X∩¬Y", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q1c", "answer_route": "/is2/#tee/papers/paper-2025-april/q1c", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "5a4cfd51add7c49fce754c3faca7d6055d3cb093ea85bed1e05c03463d8c11d5" }, { "label": "Q1(d)", "marks": 5, "source_pages": [ 2 ], "unit": 4, "topic": "Cable-company Markov matrix and diagram", "prompt": "A city is served by two cable TV companies, BestTV and CableCast. Due to their aggressive sales tactics, each year 40% of BestTV customers switch to CableCast; the other 60% of BestTV customers stay with BestTV. On the other hand, 30% of the CableCast customers switch to Best TV. The two states in this example are BestTV and CableCast. Express the information above as a transition matrix which displays the probabilities of going from one state into another state. Draw the State diagram for the above information.", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q1d", "answer_route": "/is2/#tee/papers/paper-2025-april/q1d", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "5a97ade6ae6e5acbd86601bc4dbfaa58ce41180bf5b905fe982b8295b5d3cd04" }, { "label": "Q2(a)", "marks": 10, "source_pages": [ 2 ], "unit": 2, "topic": "Fuzzy investment-controller design and centroid", "prompt": "Create a fuzzy logic system to assist in investment decision-making. The system should consider variables such as risk tolerance, and historical performance to provide recommendations on asset allocation and investment strategies. Input variables: Risk Tolerance (range 1-100) and historical performance (range1-100). Output variable is Asset allocation(range 1-100) (Conservative, Balanced and Aggressive). Use 3 descriptors for input as well as output. Set rules to accurately predict the output. Show defuzzification for risk 40 and historical performance 8.", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "Design question: all breakpoints, nine rules and Mamdani inference declared; actual printed history8 retained. Exact rational centroid independently checked.", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q2a", "answer_route": "/is2/#tee/papers/paper-2025-april/q2a", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "ee96ad33887ae0a1833efd7285a67848763278b4f6c38d48c87ff4827904fd78" }, { "label": "Q2(b)", "marks": 10, "source_pages": [ 2 ], "unit": 1, "topic": "GMM versus k-means, log likelihood and parameter count", "prompt": "With reference to Gaussian Mixture model (GMM), answer the following Compare GMM with K-means algorithm Derive the equation for Log likelihood function. What will be the no of parameters to be computed if K=5", "type": "theory", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "K5 alone does not specify dimension/covariance. Distinguishes14free vs15stored in1D; generalfullcovariance formula provided.", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q2b", "answer_route": "/is2/#tee/papers/paper-2025-april/q2b", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "cafa7954e8e33b2de7da9283cb5f46f442d957637d17b26293a8392bf9554cc9" }, { "label": "Q3(a)", "marks": 10, "source_pages": [ 3 ], "unit": 1, "topic": "Bayesian-network conditional cough probability", "prompt": "{{fig:legacy25-bayes}} Original graph: Smoking→Lung Cancer→Cough; Pneumonia→Cough and Pneumonia→Fever. Smoking S P(S) T 0.3 F 0.7 Smoking S P(C=T|S) P(C=F|S) T 0.1 0.9 F 0.01 0.99 Pneumonia P P(P) T 0.3 F 0.7 C P P(Co=T|C,P) P(Co=F|C,P) T T 0.95 0.05 T F 0.8 0.2 F T 0.7 0.3 F F 0.1 0.9 P P(Fever=T|P) P(Fever=F|P) T 0.9 0.1 F 0.01 0.99 Given the above tables for the Bayesian network, calculate Probability of a person having cough given he doesn’t have lung cancer but Smokes.", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q3a", "answer_route": "/is2/#tee/papers/paper-2025-april/q3a", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "97155e99376dffb4ed7efa0794d40004cf26da0a7a0072a998d9be2e76081fdd" }, { "label": "Q3(b)", "marks": 10, "source_pages": [ 4 ], "unit": 2, "topic": "Fuzzy Cartesian relations and composition-domain ambiguity", "prompt": "Consider 2 fuzzy sets A={0.8/small+0.5/medium+0.2/large} B={0.6/slow+0.9/moderate+0.4/fast} Find the fuzzy relation R=A×B (Cartesian Product) Given another fuzzy set C={0.7/small+0.3/medium+0.5/large}. Find the fuzzy relation S=C×B Calculate the max-min composition S∘R", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "Both originalrelationsX→Y. Literaltypedcompositionnotdefined; explicitindexconventionS⊙R workedall9cells; reverseconvention and validR⊙ST repair separately distinguished.", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q3b", "answer_route": "/is2/#tee/papers/paper-2025-april/q3b", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "92cae8abc97108bb839345acfbcebdc5829370cca6a5871f7daa8f7bfea4f331" }, { "label": "Q4(a)", "marks": 10, "source_pages": [ 4 ], "unit": 3, "topic": "PSO one iteration with missing initialization", "prompt": "Maximize the F(x) = x1² − x1.x2 + x2² + 3x1 + 4x2 + 5 using Particle Swarm Optimization. Take values W=0.9, c1=c2=1.5, r1=0.5949,r2=0.0855. Optimize and give solution after 1 iteration showing step by step process.", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "Noinitialswarmprovided. Illustrative2particleupdate explicitlyassumed; unboundedunrestrictedmaximization disclosed,not claimed solvedglobally.", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q4a", "answer_route": "/is2/#tee/papers/paper-2025-april/q4a", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "a157d9754303499d4c0c921b44968a2e3c03421418adeb2491dc0eba30f04b01" }, { "label": "Q4(b)", "marks": 10, "source_pages": [ 4 ], "unit": 1, "topic": "Naive Bayes product-purchase classification", "prompt": "Age Income Level Previous Purchase Buy Product Young Low No No Middle-aged Medium Yes Yes Senior High No No Young Medium Yes Yes Senior Low No No Middle-aged High Yes Yes Young Low Yes Yes Senior Medium No No A company wants to predict whether a customer will buy a product, based on the following features: Age (Young, Middle-aged, Senior), Income Level (Low, Medium, High), Previous Purchase (Yes, No). The output is Buy Product (Yes, No). Given a new customer with the following attributes: Age=Young, Income Level=High, Previous Purchase=No. Use Naive Bayes to predict whether this customer will buy the product.", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q4b", "answer_route": "/is2/#tee/papers/paper-2025-april/q4b", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "a59a2e85ad0ef81b9044dd3e3c1bf508c555cdd91898b3e42e23b0a164d5df90" }, { "label": "Q5(a)", "marks": 10, "source_pages": [ 5 ], "unit": 3, "topic": "Binary GA crossover, one-bit mutation and fitness", "prompt": "Implement genetic algorithm to maximize the function f(x)=x³−4x²+6x with x in the range [0,15]. Take 4 bit representation of10,12,7 and3 as the parent chromosomes. Crossover is after2nd bit and mutation is by flipping any one bit. Repeat the process for2 offspring also comment on the fitness of the offspring.", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "Pairing andbit4mutations explicitlychosen. Both suppliedparentpairsworked(twochildreneach); ambiguous2offspringwording disclosed,not called2generations.", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q5a", "answer_route": "/is2/#tee/papers/paper-2025-april/q5a", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "4a3f56588b44ef89825dc7d76b35900f84ee5cb4e4720e3fba8c87435ffcddcf" }, { "label": "Q5(b)", "marks": 10, "source_pages": [ 5 ], "unit": 4, "topic": "Bike-sharing Markov transitions, retention and gains", "prompt": "Around600 cities globally have bike-share programs where users borrow and return bikes at various stations. The distribution of bikes changes daily as they move between stations. Considering three stations (A,B,C), we can track bike movement from midnight to the next midnight. Out of the bikes borrowed from station A,30% are returned to station A,50% end up at station B,and20% end up at station C. Out of the bikes borrowed from station B,10% end up at station A,60% have been returned to station B,and30% end up at station C. Out of the bikes borrowed from station C,10% end up at station A,10% end up at station B,and80% are returned to station C. Develop a Transition Probability matrix Interpret the state transition matrix regarding a) Retention & loss b) Retention & gain Draw the State transition diagram", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q5b", "answer_route": "/is2/#tee/papers/paper-2025-april/q5b", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "b8fca0e24bc109494ea12e183b929bab00ee639a591719c7f2ee518bfc05b6a0" }, { "label": "Q6(a)", "marks": 10, "source_pages": [ 5, 6 ], "unit": 3, "topic": "ACO tour lengths, evaporation and pheromone deposition", "prompt": "For the four city Travelling salesman problem, the pheromone and cost matrix (A,B,C,D in that order given by row no,and column no) are given by Pheromone A B C D A 0 15 5 4 B 15 0 4 8 C 5 4 0 1 D 4 8 1 0 Cost A B C D A 0 10 4 3 B 10 0 1 2 C 4 1 0 1 D 3 2 1 0 Ant L1 travels from A-B-C-D-A and Ant L1 travels from A-C-B-D-A and Taking vaporization co-efficient as0.5,calculate the following:1.The length of each path 2.Pheromone level for each path", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "Qdepositmissing; symbolicQanswerandexplicitQ1illustration. DuplicateAntL1labelpreservedinprompt; treatedastwolistedants. Symmetricedgeconventiondeclared.", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q6a", "answer_route": "/is2/#tee/papers/paper-2025-april/q6a", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "0ab83790dc136ba00f28bc77d4669558343948535e9cffdacac1a144fbf74855" }, { "label": "Q6(b)", "marks": 10, "source_pages": [ 6 ], "unit": 4, "topic": "Two-step walking/cycling Markov transition", "prompt": "A student walks to school, or he rides his bicycle. If he walks to school one day, then the next day he will walk or cycle with equal probability. But if he bicycles one day, then the probability that he will walk next day is1/4. Express this information in a transition matrix. if it is assumed that the initial day is Monday,write a matrix that gives probabilities of a transition from Monday to Wednesday.", "type": "numerical", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q6b", "answer_route": "/is2/#tee/papers/paper-2025-april/q6b", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "41ce469e886a4762fd9cf58774fc4861679dba69afaa8bfe1227af6a59f060bb" }, { "label": "Q7(a)", "marks": 10, "source_pages": [ 6 ], "unit": 2, "topic": "Fuzzy logic applications and complete controller example", "prompt": "List the applications of Fuzzy logic. Explain one in detail.", "type": "theory", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q7a", "answer_route": "/is2/#tee/papers/paper-2025-april/q7a", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "88fa357cf31cdd80d8fec62083589a08b16c95b25ade215c02bbc80c0758c431" }, { "label": "Q7(b)", "marks": 10, "source_pages": [ 6 ], "unit": 4, "topic": "Bellman equations and reinforcement-learning applications", "prompt": "Explain Bellman equation in reinforcement learning. Also give applications of reinforcement learning.", "type": "theory", "scope": "current-syllabus", "solution_coverage": "complete", "coverage_notes": "", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "id": "is2-2025-04-28-q7b", "answer_route": "/is2/#tee/papers/paper-2025-april/q7b", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "9500c725b82950148c9b8b4aa07056a5961f6b62533434b74962a8adb3ef9dd5" } ], "source_limitations": "Some questions omit parameter choices or contain ambiguous/inconsistent source data. The relevant answer distinguishes printed data from assumptions and calculates valid alternatives; all source questions remain represented." }, { "source_id": "is2-16", "date": "2024-06-25", "exam_type": "reexam", "academic_year": "2023-24", "chapter_id": "paper-2024-reexam", "title": "Re-examination · 25 Jun 2024", "id": "is2-2024-06-25", "batch": "Historical Semester VI", "source_file": "Intelligent Systems-Re-exam 2023-24.pdf", "source_sha256": "4301ddc2e84ba9daedb99cd6b5d3fd88419c89280bc72d86e49f3a34bf5844e6", "source_pages": 6, "visual_review": { "status": "reviewed", "receipt": "audit/is2_older_papers_20261014.md", "note": "All six pages newly visually inspected; table values, diagrams, marks and continuations transcribed." }, "pattern": { "time_minutes": 180, "offered_marks": 140, "attempted_marks": 100, "compulsory_questions": [ "Q1" ], "choose": 4, "from": [ "Q2", "Q3", "Q4", "Q5", "Q6", "Q7" ], "whole_question_marks": 20 }, "questions": [ { "label": "Q1(a)", "marks": 5, "source_pages": [ 1 ], "unit": 3, "topic": "Traditional versus evolutionary optimization", "prompt": "Compare traditional optimization and evolutionary optimization algorithms. Illustrate the basic framework of evolutionary algorithms.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q1a", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q1a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "4c8d551f5014a24ef87892874406f069865e29ec5970b58009d1fe1ce1fcb0e8" }, { "label": "Q1(b)", "marks": 5, "source_pages": [ 1 ], "unit": 1, "topic": "Bayes posterior for red ball", "prompt": "Bag A contains 3 red and 4 green balls. Bag B contains 4 red and 5 green balls. One ball is drawn at random from one of the bags and found red. Find the probability it came from Bag A.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "Equal bag-selection probability explicitly assumed.", "id": "is2-2024-06-25-q1b", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q1b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "6d90bd7c98fcd1cec9a8f28d81ff9ca282a2e52d031c3be0bc2cdcc8a187df41" }, { "label": "Q1(c)", "marks": 5, "source_pages": [ 1 ], "unit": 2, "topic": "Fuzzy union, intersection and complements", "prompt": "A={(x₁,.7),(x₂,.3),(x₃,.9),(x₄,.1)} and B={(x₁,.2),(x₂,.5),(x₃,.7),(x₄,.4)}. Find (1) A∪B, (2) A∩B, (3) Ā∩B̄, (4) Ā∪B̄.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q1c", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q1c", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "e50126447fbb3b8a7f05390c459c3a87f54eb524bfa6e5b9cf9cd212f9916db7" }, { "label": "Q1(d)", "marks": 5, "source_pages": [ 1 ], "unit": 4, "topic": "Irreducibility and stationary distribution", "prompt": "For a two-state Markov chain with P=[[0.8,0.2],[0.3,0.7]], (a) is the chain irreducible? (b) find its stationary distribution.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q1d", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q1d", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "4f54da566593b42b3db1e65a6879ab8e6c6059d55ccabedcbda5e96eae78af5d" }, { "label": "Q2(a)", "marks": 10, "source_pages": [ 2 ], "unit": 1, "topic": "GMM modified log likelihood and parameters", "prompt": "With reference to the Gaussian mixture model: (1) explain the need for the modified log-likelihood function; (2) how is it solved? (3) for K=6 Gaussian distributions estimate the number of parameters to compute.", "type": "mixed", "scope": "current-syllabus", "coverage_notes": "Dimension/covariance type unspecified; general formula and stated1D17free/18stored counts supplied.", "id": "is2-2024-06-25-q2a", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q2a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "0ac92da07bce2e597c4878417d6d1f3475f273d06c7676dca964acd1fdefaef9" }, { "label": "Q2(b)", "marks": 10, "source_pages": [ 2 ], "unit": 2, "topic": "Fuzzy washing-machine controller", "prompt": "Design a domestic washing-machine controller with Dirt and Grease inputs, both 0–100, and Wash time output 0–100. Use low/medium/high input terms and very short/short/medium/high/very high output terms. Derive rules, support the design with graphs and show that larger dirt gives longer wash time. Defuzzify for Dirt=60% and Grease=70%.", "type": "design", "scope": "current-syllabus", "coverage_notes": "Memberships and complete rule base explicitly assumed; exact piecewise centroid calculated.", "id": "is2-2024-06-25-q2b", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q2b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "32e4885a9ae5e007cd7c22aef32fdde4991f8945b86f9d4f0c87c3e27ee24c92" }, { "label": "Q3(a)", "marks": 10, "source_pages": [ 2 ], "unit": 2, "topic": "Fuzzy relation and max-product comparison", "prompt": "D={0.3/0 + 0.7/1 + 1/2}, I={0.5/20 + 0.6/30 + 0.8/40}, V={0.6/20 + 0.5/30 + 0.4/40}. Determine (1) R=D×I, (2) max–min composition of V∘R, (3) max–product composition of V∘R.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q3a", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q3a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "32a5733ff3ce1dceee07f6ad1a86054878286eb1c2530789cb3a9ec6ba5f397e" }, { "label": "Q3(b)", "marks": 10, "source_pages": [ 2, 3 ], "unit": 3, "topic": "PSO maximization, supplied parameters", "prompt": "Find the maximum of f(x)=−x²+5x+20 on −6≤x≤4 using PSO. Use four initial positions −5.6,−5.2,−1.1,2.3. Show detailed computations for iteration1, with c₁=c₂=1, r₁=r₂=.5, w=1 and all initial velocities zero.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q3b", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q3b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "bad4eaad205f3c44f1ebea576d8227b9c52f1a9515e91acb7abf8a1601ba0aa4" }, { "label": "Q4(a)", "marks": 10, "source_pages": [ 3 ], "unit": 1, "topic": "Naive Bayes and Laplace smoothing", "prompt": "(1) Use Naive Bayes on the dataset to predict whether a person buys a computer for Age=Old, Income=Medium, Freelancer=Yes. (2) Explain Laplacian smoothing, whether it is required here and its impact. ID Age Income Freelancer Buys computer 1 Young High No No 2 Young High No No 3 Middle Age High No Yes 4 Old Medium No Yes 5 Old Low Yes Yes 6 Old Low Yes No 7 Middle Age Low Yes Yes 8 Young Medium No No 9 Young Low Yes Yes 10 Young Medium Yes Yes", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q4a", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q4a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "72f5c37cee2c2efce484a8b9fc1d2cbcc67fd6866c8de905145fa62e003c55a4" }, { "label": "Q4(b)", "marks": 10, "source_pages": [ 3 ], "unit": 4, "topic": "Transition matrix properties and two-step probabilities", "prompt": "(1) Discuss how state transitions are represented by a transition matrix. (2) Cite and illustrate any3 properties. (3) Given P=[[1/2,1/2],[3/4,1/4]] and initial row distribution p⁽⁰⁾=[1/4,3/4], define and find (a) P₂₁⁽²⁾, (b) P₁₂⁽²⁾, (c) P⁽²⁾, (d) p₁⁽²⁾.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q4b", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q4b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "86210ea01076db515126c2e0d9e0bbba1c7675859c67a70d1c6430b908ed9d81" }, { "label": "Q5(a)", "marks": 10, "source_pages": [ 4 ], "unit": 3, "topic": "GA selection, crossover and survivor replacement", "prompt": "Use the population table. (1) Rank-select the best two, then one-point crossover in the middle. (2) A three-way tournament on individuals1,3,5 selects parent1; a two-way tournament on2,4 selects parent2. Again crossover in the middle. (3) Replace using fitness-based survivor selection and compute the new average. (4) Repeat replacement from the original population using age-based survivor selection and compute its average. No. Chromosome Fitness Age 1 01000000 64 8 2 01011111 95 2 3 01010000 80 13 4 01011001 89 9 5 01011110 94 4 6 01001111 79 10 7 01010001 81 12 8 01000001 65 15 9 01011000 88 10 10 01000000 64 20", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q5a", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q5a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "6ae09008f7280a0613763a5b96f64147ec93983f43e69e04914a1ffb2c955d82" }, { "label": "Q5(b)", "marks": 10, "source_pages": [ 4, 5 ], "unit": 2, "topic": "Mean of maxima and centroid of trapezoids", "prompt": "For the given membership functions determine the crisp output using (1) Mean of Maxima and (2) Centroid / Center of Area. {{fig:legacy24r-defuzz-a}} First printed membership function: height0.75, corners x=0,2,3,4. {{fig:legacy24r-defuzz-b}} Second printed membership function: height1, corners x=0,3,4,7.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "Per-graph reading primary; explicitly stated max-aggregation alternative also calculated.", "id": "is2-2024-06-25-q5b", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q5b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "700ef7232baaed2107fdbffcdf51552bfae982983972f5245bd91fccacb592d4" }, { "label": "Q6(a)", "marks": 10, "source_pages": [ 5 ], "unit": 3, "topic": "Genetic algorithm for four-city TSP", "prompt": "For a genetic algorithm applied to a4-city TSP: (1) cite how to initialize a population; (2) explain fitness calculation; (3) describe common crossover and mutation operators.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q6a", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q6a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "44ef28533e7a2f08b8462554277eabe77552f780fafa12eef647aa2385764240" }, { "label": "Q6(b)", "marks": 10, "source_pages": [ 5, 6 ], "unit": 4, "topic": "Markov process and homework transition paths", "prompt": "(I) Define the Markov process and list applications. (II) A professor assigns full-credit F, half-credit H and no-credit N homework according to the transition diagram. (a) Construct the TPM; (b) find P(X₃=F|X₂=N); (c) P(X₂=N|X₁=H); (d) P(X₄=H|X₂=F). {{fig:legacy24r-homework}} Redrawn transition diagram. Each arrow goes from the current state to the next state; labels are probabilities.", "type": "mixed", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q6b", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q6b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "81b2df40cab2b1c2ea7c3ce28aaa7c4b140d21f0e5b057b655d58c5c81513064" }, { "label": "Q7(a)", "marks": 5, "source_pages": [ 6 ], "unit": 1, "topic": "Expectation Maximization algorithm", "prompt": "Discuss the Expectation Maximization(EM) algorithm.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q7a", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q7a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "f882cea129eff430f83d833c51f592034ec4466164a3d9bd3efb3057548975ad" }, { "label": "Q7(b)", "marks": 5, "source_pages": [ 6 ], "unit": 2, "topic": "Lambda-cut of a fuzzy relation", "prompt": "For the fuzzy relation R below, find the λ-cut relations for λ=.003 and λ=.5. \\[R=\\begin{bmatrix}1&0.1&0&0.4\\\\0.001&0.5&0.5&0.6\\\\0.3&1&0.5&0\\\\0&0.4&0.002&0.1\\end{bmatrix}\\]", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q7b", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q7b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "ae581a69db1b39525ffea7254ee8208a3b87a54e857e16fdf3f8c285a1ee9536" }, { "label": "Q7(c)", "marks": 5, "source_pages": [ 6 ], "unit": 3, "topic": "ACO path length and pheromone update", "prompt": "For the four-city travelling salesman problem, row and column order is A, B, C, D. The pheromone and cost matrices are: \\[\\tau=\\begin{bmatrix}0&15&5&4\\\\15&0&4&8\\\\5&4&0&1\\\\4&8&1&0\\end{bmatrix}\\]\\[D=\\begin{bmatrix}0&10&4&3\\\\10&0&1&2\\\\4&1&0&1\\\\3&2&1&0\\end{bmatrix}\\] Ant L₁ travels A–B–C–D–A and ant L₂ travels A–C–B–D–A. Taking the vaporization coefficient as 0.5, calculate (1) the length of each path and (2) pheromone level for each path.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "DepositconstantQnotgiven;Q=1explicitassumption,edgeupdatesallshown.", "id": "is2-2024-06-25-q7c", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q7c", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "f6f11292a572ee8c5a52ad1e12950362ff4d21908b507ed1db63151079393db9" }, { "label": "Q7(d)", "marks": 5, "source_pages": [ 6 ], "unit": 4, "topic": "Bellman equation", "prompt": "Explain the Bellman equation in reinforcement learning.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-06-25-q7d", "answer_route": "/is2/#tee/papers/paper-2024-reexam/q7d", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "476ee9f4ae836aee406da622c661059a5c6ba9c8220efe4d1cf57861e948bd7b" } ], "source_limitations": "Some questions omit parameter choices or contain ambiguous/inconsistent source data. The relevant answer distinguishes printed data from assumptions and calculates valid alternatives; all source questions remain represented." }, { "source_id": "is2-14", "date": "2024-04-17", "exam_type": "final", "academic_year": "2023-24", "chapter_id": "paper-2024-final", "title": "Final examination · 17 Apr 2024", "id": "is2-2024-04-17", "batch": "Historical Semester VI", "source_file": "Intelligent System -II, Sem-VI, Final Exam 2023-24.pdf", "source_sha256": "94fe669b4ab160053096a24bbfe8ccb5d47ce22bced0a16181ece3c263566c02", "source_pages": 6, "visual_review": { "status": "reviewed", "receipt": "audit/is2_older_papers_20261014.md", "note": "All six pages newly visually inspected; table values, diagrams, marks and continuations transcribed." }, "pattern": { "time_minutes": 180, "offered_marks": 140, "attempted_marks": 100, "compulsory_questions": [ "Q1" ], "choose": 4, "from": [ "Q2", "Q3", "Q4", "Q5", "Q6", "Q7" ], "whole_question_marks": 20 }, "questions": [ { "label": "Q1(a)", "marks": 5, "source_pages": [ 1 ], "unit": 1, "topic": "Conditional probability from fruit counts", "prompt": "Consider the given dataset, apply Bayes rule and predict if a fruit has the following properties. Find (a) P(Yellow | Mango), (b) P(Sweet | Banana). Fruit Yellow Sweet Long Total Mango 350 450 0 650 Banana 400 300 350 400 Others 50 100 50 150 Total 800 850 400 1200", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q1a", "answer_route": "/is2/#tee/papers/paper-2024-final/q1a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "b09e807608d34f054c19b3baaf9195fb01329426efb2b510615290917f4845ad" }, { "label": "Q1(b)", "marks": 5, "source_pages": [ 1 ], "unit": 2, "topic": "Membership function and age sketch", "prompt": "Define a membership function in the context of fuzzy logic. Using your intuition, sketch fuzzy membership functions for the “age of people”.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q1b", "answer_route": "/is2/#tee/papers/paper-2024-final/q1b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "449d877290da4970bc9a66e00915ec1a966042d67fc496e50e84303074b3e85c" }, { "label": "Q1(c)", "marks": 5, "source_pages": [ 1 ], "unit": 3, "topic": "Mutation operators", "prompt": "Cite and illustrate various mutation operators in the genetic algorithm.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q1c", "answer_route": "/is2/#tee/papers/paper-2024-final/q1c", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "5c675c085b2476dea0ae196940e61c265c6848dcfe3678bc3cd4754b7edd5d6b" }, { "label": "Q1(d)", "marks": 5, "source_pages": [ 1, 2 ], "unit": 4, "topic": "Markov chain diagram and path probability", "prompt": "Consider the Markov chain with S={1,2,3} and transition matrix below. (a) Draw the state transition diagram. (b) The scan prints “P(X₁=1)=(X₂=2)=1/4”; calculate P(X₁=3, X₂=2, X₃=1). \\[P=\\begin{bmatrix}1/2&1/4&1/4\\\\1/3&0&2/3\\\\1/2&1/2&0\\end{bmatrix}\\]", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "Ambiguous printed time subscript; both literal and corrected readings solved.", "id": "is2-2024-04-17-q1d", "answer_route": "/is2/#tee/papers/paper-2024-final/q1d", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "ce2aa601b7aaf64494045effa59d2fbe0550e98571a06be7e1e99c0d96f3d49a" }, { "label": "Q2(a)", "marks": 10, "source_pages": [ 2 ], "unit": 1, "topic": "Bayesian network joint probabilities", "prompt": "Given the task of modelling a student’s marks for an exam, use the Bayesian network and conditional probability tables below. Exam E: 0 easy, 1 difficult. IQ I: 0 low, 1 high. Aptitude S: 0 low, 1 high. (a) Calculate the probability of a difficult exam, low IQ, low aptitude, passing and securing admission. (b) Calculate the probability of high IQ and aptitude, an easy exam, failing and no admission. {{fig:legacy24-exam-bn}} Redrawn transition diagram. Each arrow goes from the current state to the next state; labels are probabilities. Root P(0) P(1) E 0.7 0.3 I 0.8 0.2 I E P(M=0) P(M=1) 0 0 0.6 0.4 0 1 0.9 0.1 1 0 0.5 0.5 1 1 0.8 0.2 I P(S=0) P(S=1) 0 0.75 0.25 1 0.4 0.6 M P(A=0) P(A=1) 0 0.6 0.4 1 0.9 0.1", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "Pass/admission coding is not explicitly printed; assumption and conditional alternative stated.", "id": "is2-2024-04-17-q2a", "answer_route": "/is2/#tee/papers/paper-2024-final/q2a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "91933d438c6299d764bd08871443912251747cd4b01c3085c9e6653fa5e819da" }, { "label": "Q2(b)", "marks": 10, "source_pages": [ 3 ], "unit": 2, "topic": "Fuzzy Cartesian product and composition", "prompt": "A={1/low + 0.2/medium + 0.6/high}; B={0.9/positive + 0.3/zero + 0.8/negative}. (1) Find R=A×B. (2) With C={0.3/low + 0.4/medium + 0.6/high}, find S=C×B. (3) Find S∘R using max–min composition.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "Printed composition has incompatible named intermediate universes; explicit matrix conventions and typed alternative supplied.", "id": "is2-2024-04-17-q2b", "answer_route": "/is2/#tee/papers/paper-2024-final/q2b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "a3d450712a537a3f9ed99a417b64b41faa0dce9614b6c76f6bb887ef65def4ce" }, { "label": "Q3(a)", "marks": 10, "source_pages": [ 3 ], "unit": 1, "topic": "GMM versus K-means", "prompt": "Discuss the significance of using Gaussian Mixture Models (GMMs) over K-means clustering in capturing complex data distributions.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q3a", "answer_route": "/is2/#tee/papers/paper-2024-final/q3a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "b301c2eef14b1494f3afe5ee1955c87a64ed43d644307589c3722946cb1a5e3d" }, { "label": "Q3(b)", "marks": 10, "source_pages": [ 3 ], "unit": 2, "topic": "Center of sums defuzzification", "prompt": "For the given membership functions, determine the defuzzified crisp output using the Center of Sums method. {{fig:legacy24-centre-sums}} Source graph redrawn: A1 corners (1,0),(3,0.5),(7,0.5),(8,0); A2 corners (3,0),(4,0.3),(8,0.3),(9,0).", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q3b", "answer_route": "/is2/#tee/papers/paper-2024-final/q3b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "17de0a4437dcb540ec82df30685f44dc9167a9bb4e11fc89ea78199282f35bcd" }, { "label": "Q4(a)", "marks": 10, "source_pages": [ 4 ], "unit": 2, "topic": "Fuzzy quality-control design", "prompt": "Design a fuzzy logic-based quality-control system for manufacturing that detects defects and decides whether to reject or accept a product. Inputs: Pressure [0,100] and Quality of material [0,100]. Output: Defect detection [0,100]. Use three descriptors per input and five per output; draw membership functions and derive rules. If pressure and material quality are high, accept; vice versa reject. Show defuzzification for Pressure 40 and Quality 80.", "type": "design", "scope": "current-syllabus", "coverage_notes": "Memberships, rules and threshold are explicitly designed assumptions, as permitted by the question.", "id": "is2-2024-04-17-q4a", "answer_route": "/is2/#tee/papers/paper-2024-final/q4a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "efc7bf73642d7115ae5a6f2204c9ae0590a023263db2f63d83d9ad8f452b5e76" }, { "label": "Q4(b)", "marks": 10, "source_pages": [ 4 ], "unit": 3, "topic": "ACO components and roles", "prompt": "Describe the key components of an Ant Colony Optimization algorithm and their respective roles in finding optimal solutions.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q4b", "answer_route": "/is2/#tee/papers/paper-2024-final/q4b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "fd0f5ad39c1ffee8beed2b5ecafa4075b71a7d31d9652486298acfaf5c15a33d" }, { "label": "Q5(a)", "marks": 10, "source_pages": [ 4 ], "unit": 3, "topic": "PSO inspiration, parameters and one iteration", "prompt": "With reference to PSO: (1) justify its inspiration; (2) list and briefly explain its parameters; (3) minimize f(x)=3x²+4x+10 with five particles initially at −9, −6, −2, 8, 10. Assume suitable parameter values and estimate positions after one iteration.", "type": "mixed", "scope": "current-syllabus", "coverage_notes": "Question permits parameters to be assumed; one explicit synchronous iteration shown.", "id": "is2-2024-04-17-q5a", "answer_route": "/is2/#tee/papers/paper-2024-final/q5a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "b97abd746d6f01d2683ef8b670db75d59c030e6b587095291a3a39f001ad4613" }, { "label": "Q5(b)", "marks": 10, "source_pages": [ 4 ], "unit": 4, "topic": "Brand-switching transition matrix", "prompt": "Two cold-drink brands A and B are sold. An A buyer has 80% chance of buying A next. A B buyer has 90% chance of buying B next. (I) Develop the transition matrix. (II) Interpret retention and loss, and retention and gain. (III) Draw the transition diagram.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q5b", "answer_route": "/is2/#tee/papers/paper-2024-final/q5b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "1b77d0deda717533394ac6583813852bcd381fc0f3f1ee083e6d08f20919d799" }, { "label": "Q6(a)", "marks": 10, "source_pages": [ 5 ], "unit": 3, "topic": "GA ranking and three crossover methods", "prompt": "A chromosome x=abcdefghij has ten genes, each a digit 0–9. Fitness f(x)=(a+b)−(c+d)+(e+f)+(g+h)−(i+j). Initial chromosomes: x₁=6541353253; x₂=8712660124; x₃=2392128546; x₄=4185209426. (1) Evaluate and rank fitness. (2) Cross the fittest two at the midpoint. (3) Cross second and third fittest using two points b and f. (4) Cross first and third using arithmetic crossover α=.5. (5) Evaluate all six offspring. (6) Has overall fitness improved?", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "Arithmetic crossover conflicts with digit-only encoding; raw requested values retained and issue stated.", "id": "is2-2024-04-17-q6a", "answer_route": "/is2/#tee/papers/paper-2024-final/q6a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "61f7a95beebeb0644af8eb323569e7376bf39320f64f84c8fcbc5c0d0f4af959" }, { "label": "Q6(b)", "marks": 10, "source_pages": [ 5, 6 ], "unit": 4, "topic": "Markov property and weather path", "prompt": "Describe the concept of a Markov process and its use in probabilistic systems. For the given state diagram S,C,R: (a) today is sunny; find probability tomorrow is sunny and the next day rainy. (b) today is cloudy and yesterday rainy; find probability tomorrow is sunny. {{fig:legacy24-weather}} Redrawn transition diagram. Each arrow goes from the current state to the next state; labels are probabilities.", "type": "mixed", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q6b", "answer_route": "/is2/#tee/papers/paper-2024-final/q6b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "f7b3f4a09fcc4635b9af7f8b10d2313fefc802a5f28a73535d1abe8d12b1cf3a" }, { "label": "Q7(a)", "marks": 5, "source_pages": [ 6 ], "unit": 1, "topic": "Bayes bag posterior", "prompt": "Bag B₁ contains 3 white and 7 black balls. Bag B₂ contains 5 white and 3 black balls. A ball drawn from one of the bags is white. Find the probability it came from B₁.", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "Equal bag-selection priors explicitly assumed.", "id": "is2-2024-04-17-q7a", "answer_route": "/is2/#tee/papers/paper-2024-final/q7a", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "683368f1f48b0b12bac507f5f8344a0b0922dcb466d536fab55569c585cab307" }, { "label": "Q7(b)", "marks": 5, "source_pages": [ 6 ], "unit": 2, "topic": "Max–min relational composition", "prompt": "For the following fuzzy relations R and S, obtain T using max–min composition. \\[R=\\begin{bmatrix}0.6&0.3\\\\0.2&0.5\\end{bmatrix},\\quad S=\\begin{bmatrix}1&0.4&0.5\\\\0.8&0.3&0.8\\end{bmatrix}\\]", "type": "numerical", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q7b", "answer_route": "/is2/#tee/papers/paper-2024-final/q7b", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "789462b411c328b4a120d23015f2c9898fd279926bf606fe40101b8e37e91da6" }, { "label": "Q7(c)", "marks": 5, "source_pages": [ 6 ], "unit": 3, "topic": "Point versus uniform crossover", "prompt": "Differentiate between point crossover and uniform crossover.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q7c", "answer_route": "/is2/#tee/papers/paper-2024-final/q7c", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "797507639c48e216ba42f4a650c08121e6f06ff92abf6200bfd8129b29b62e96" }, { "label": "Q7(d)", "marks": 5, "source_pages": [ 6 ], "unit": 4, "topic": "Applications of reinforcement learning", "prompt": "Discuss the applications of reinforcement learning.", "type": "theory", "scope": "current-syllabus", "coverage_notes": "", "id": "is2-2024-04-17-q7d", "answer_route": "/is2/#tee/papers/paper-2024-final/q7d", "marks_status": "printed, visually checked", "prompt_status": "Full question, data and tasks transcribed from original scan; grammar and layout normalized; figures redrawn and coordinate tables preserved.", "solution_coverage": "complete", "solution_review": "audit/is2_older_papers_20261014.md", "answer_html_sha256": "c9a128c1a15b2b40be95eaf28bffc9b3f129d373f47db2b1bef3c380ced6b6eb" } ], "source_limitations": "Some questions omit parameter choices or contain ambiguous/inconsistent source data. The relevant answer distinguishes printed data from assumptions and calculates valid alternatives; all source questions remain represented." } ], "catalogue_candidates": [], "recovered_source_inventory": [ { "source_id": "is2-13", "source_file": "Intelligent Systems-II, Sem-V, Year 2025-26, Final Exam.pdf", "sha256": "6f4daf8a75c0611d1b8e83faa3966260d7d65726eaffb4ba57b4f20ef3d13217", "pages": 6, "status": "duplicate scan of represented paper", "paper_id": "is2-2025-11-24", "archive_copy_count": 2 }, { "source_id": "is2-14", "source_file": "Intelligent System -II, Sem-VI, Final Exam 2023-24.pdf", "sha256": "94fe669b4ab160053096a24bbfe8ccb5d47ce22bced0a16181ece3c263566c02", "pages": 6, "status": "mapped distinct paper", "paper_id": "is2-2024-04-17", "archive_copy_count": 2 }, { "source_id": "is2-15", "source_file": "Intelligent Systems -II, Sem VI, Final Exam 2024-25 & Re Exam 2023-24.pdf", "sha256": "f472be862d2d78c06cab82d50e939ee676ffb7f2e97707662cf13b151eaa9f51", "pages": 6, "status": "mapped distinct paper", "paper_id": "is2-2025-04-28", "archive_copy_count": 2 }, { "source_id": "is2-16", "source_file": "Intelligent Systems-Re-exam 2023-24.pdf", "sha256": "4301ddc2e84ba9daedb99cd6b5d3fd88419c89280bc72d86e49f3a34bf5844e6", "pages": 6, "status": "mapped distinct paper", "paper_id": "is2-2024-06-25", "archive_copy_count": 2 } ] }