Experiencing MIS
7th Edition
ISBN: 9780134380421
Author: KROENKE
Publisher: PEARSON
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Chapter 4, Problem 2ARQ
Explanation of Solution
New hardware affects the competitive strategies:
Generally, organizations get quite interested to use new hardware for their revenue generation based on potential opportunities or threats.
Internet of Things (IoT):
Internet of Things is a design where objects are linked with internet for interaction purposes. That is, interact with “devices”, “services”, or “applications”.
- Each and every day objects are embedded with hardware that used for “sensing”, “processing”, and “transmitting” data.
- Then objects are linked with internet to share the data with any other application, device, or service.
Smart device:
Smart device is a device which is added with extra features such as more processing power, more memory, Wi-Fi connectivity, net access, and so on.
Reason to use the smart devices:
- Nowadays, smart devices are essential. Because it is added with extra features such as more processing power, more memory, Wi-Fi connectivity, net access, and so on.
- People have started using smart phone much more than before.
- It helps to change the way of business operations.
- Business need to enhance the existing revenue with the help of smart devices.
Examples of how businesses could profit from smart device:
- One example is business from General Electric’s (GE) industrial internet.
- GE industrial internet is the broad plan which mainly focused to create the smart devices, investigating the data from these devices, and making changes to increase efficiencies, minimize the waste, and to enhance the decision making.
- The industry experiences huge profit by using smart devices in hospitals, railroads, power grids, and so on.
- Another example is business from Microsoft.
- Microsoft industry also made tremendous profits with the help of smart devices.
- The engineers from Microsoft were capable to minimize the energy cost and increase the profits.
Self-driving cars:
Self-driving cars is also known as driverless car.
- It uses the types of sensor to navigate the car without human intervention like traditional car...
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Chapter 4 Solutions
Experiencing MIS
Ch. 4.2 - Prob. 1SWCh. 4.2 - Prob. 2SWCh. 4.2 - Prob. 3SWCh. 4.2 - Prob. 4SWCh. 4.2 - Prob. 5SWCh. 4.2 - Prob. 6SWCh. 4 - Prob. 1EGDQCh. 4 - Prob. 2EGDQCh. 4 - Prob. 3EGDQCh. 4 - Prob. 4EGDQ
Ch. 4 - Prob. 1GDQCh. 4 - Prob. 2GDQCh. 4 - Prob. 3GDQCh. 4 - Prob. 4GDQCh. 4 - Prob. 1ARQCh. 4 - Prob. 2ARQCh. 4 - Prob. 3ARQCh. 4 - Prob. 4ARQCh. 4 - Prob. 1UYKCh. 4 - Prob. 2UYKCh. 4 - Prob. 3UYKCh. 4 - Prob. 4CECh. 4 - Prob. 5CECh. 4 - Prob. 6CECh. 4 - Prob. 7CECh. 4 - Prob. 8CECh. 4 - Prob. 9CSCh. 4 - Prob. 10CSCh. 4 - Prob. 11CSCh. 4 - Prob. 12CSCh. 4 - Prob. 13CSCh. 4 - Prob. 14MMLCh. 4 - Prob. 15MML
Knowledge Booster
Similar questions
- In the diagram, there is a green arrow pointing from Input C (complete data) to Transformer Encoder S_B, which I don’t understand. The teacher model is trained on full data, but S_B should instead receive missing data—this arrow should not point there. Please verify and recreate the diagram to fix this issue. Additionally, the newly created diagram should meet the same clarity standards as the second diagram (Proposed MSCATN). Finally provide the output image of the diagram in image format .arrow_forwardPlease provide me with the output image of both of them . below are the diagrams code make sure to update the code and mentionned clearly each section also the digram should be clearly describe like in the attached image. please do not provide the same answer like in other question . I repost this question because it does not satisfy the requirment I need in terms of clarifty the output of both code are not very well details I have two diagram : first diagram code graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder…arrow_forwardWhy I need ?arrow_forward
- Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…arrow_forwardPlease provide me with the output image of both of them . below are the diagrams code I have two diagram : first diagram code graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…arrow_forwardI'm reposting my question again please make sure to avoid any copy paste from the previous answer because those answer did not satisfy or responded to the need that's why I'm asking again The knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models: First Diagram (Student A - Missing Values): Clearly illustrate the student training process. Show how knowledge distillation happens between the teacher and Student A. Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention). Second Diagram (Student B - Missing Labels): Similarly, detail the training process for Student B. Clarify how knowledge distillation works between the teacher and Student B. Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred. Since these are two distinct challenges…arrow_forward
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