EBK ESSENTIALS OF MIS
12th Edition
ISBN: 8220101459305
Author: LAUDON
Publisher: PEARSON
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Chapter 10, Problem 5RQ
Program Plan Intro
M-commerce:
- It is also called as mobile commerce.
- The m-commerce means purchasing and retailing of goods and services using wireless handheld devices.
- The handheld devices are smartphone, table and hybrid devices.
Explanation of Solution
Most important applications in m-commerce:
- The m-commerce is mainly used for location-based applications such as
- Detecting local hotels and cafeterias
- Checking local traffic and climate conditions
- Offering personalized location-based advertising
- Mobile phones and handhelds are also used in the following ways
- Pay the mobile bill
- Banking
- Securities trading
- Transportation plan updates
- Downloads of digital content
Types of m-commerce services and applications:
- Location-based services
- These type offers services to users in local areas and are based on the user’s location.
- Most of smartphone owners are used location based services.
- Three services are contains in these applications.
- Geosocial services
- Geoadvertising services
- Geoinformation services
- Banking and financial services
- Using mobile devices user can performs the following things
- Manage user account...
- Using mobile devices user can performs the following things
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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 -->…
Please 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)…
Chapter 10 Solutions
EBK ESSENTIALS OF MIS
Ch. 10.2 - Prob. 1CQ1Ch. 10.2 - Prob. 2CQ1Ch. 10.2 - Prob. 3CQ1Ch. 10.2 - Prob. 4CQ1Ch. 10.3 - Prob. 1CQ2Ch. 10.3 - Prob. 2CQ2Ch. 10.3 - Prob. 3CQ2Ch. 10.3 - Prob. 4CQ2Ch. 10 - Prob. 1RQCh. 10 - Prob. 2RQ
Ch. 10 - Prob. 3RQCh. 10 - Prob. 4RQCh. 10 - Prob. 5RQCh. 10 - Prob. 6RQCh. 10 - Prob. 7DQCh. 10 - Prob. 8DQCh. 10 - Prob. 9DQCh. 10 - Prob. 10HMPCh. 10 - Prob. 11HMPCh. 10 - Prob. 14CTPCh. 10 - Prob. 15CSQCh. 10 - Prob. 16CSQCh. 10 - Prob. 17CSQCh. 10 - Prob. 18CSQCh. 10 - Prob. 19MLMCh. 10 - Prob. 20MLM
Knowledge Booster
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