Experiencing MIS
Experiencing MIS
7th Edition
ISBN: 9780134380421
Author: KROENKE
Publisher: PEARSON
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Chapter 4, Problem 1UYK

Explanation of Solution

a.

Recommendations:

  • Having a good experience at a major internet retailer, the CPU or hard drive space that one should look for in the computer depends on one’s needs and how long they want to have the computer.
  • The minimum requirement for alternative A is Intel i3 processor.
    • The other better choices include intel i5 processor or AMD, an A10 processor would also work out...

Explanation of Solution

b.

Required software:

  • Windows is automatically given on the computer since the computer requires an operating system on it.
  • Some select titles required in addition t...

Explanation of Solution

c.

Shopping in three websites for the best computer deal:

The following assumptions must be made for all computers when looking around on the sites as given below:

  • All computers will have a wireless internet card included in them.
  • All computers will have Windows 8 or Windows 8.1 on them that are reviewed here.
  • Microsoft Office will be needed to be purchased regardless of the computer.

Given the assumptions below and an exhaustive review of the websites, the best company out of the three is Lenovo. Here, they seem to have a higher quality product for a price that is well below the budgeted limit of $1,000...

Explanation of Solution

d.

Recommendation on computer:

Nowadays, Lenovo is one of the preferred choices for computers on the marketplace. While HP is more geared towards entertainment and on the other hand, Dell is designed more for heavier productivity use...

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here is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…
details explanation and background   We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models:  Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing for some samples). We use knowledge distillation to guide both students, even when labels are missing. Why We Use Two Students Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher. Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation). Using two students allows each to specialize in…
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