
Concept explainers
Explanation of Solution
Enterprise system:
Enterprise processes provide the organization to sustain longer and also it supports the activities performed in various departments.
Example for an enterprise system:
Consider a hospital that contains an enterprise system...
Explanation of Solution
Challenges that enterprise applications pose:
Challenges of ERP:
- Cost overheads – Installation of ERP system for a business requires more money which is one of the main challenges that enterprise application pose.
- Investment in infrastructure – Allocation of suitable budget for the required system is quite difficult process.
- Appropriate training – Understanding the ERP is hard and it required more training.
Challenges of CRM:
- Training – Understanding the CRM system is hard and it required more training for the workers...
Explanation of Solution
Addressing the challenges:
- Addressing the challenge for Cost overheads: Before implementing ERP system, a detailed plan with complete breakdown of requirements should be worked out.
- Addressing the challenge for investment in infrastructure: Allocating suitable budget for infrastructure is necessary.
- Addressing the challenge for appropriate training: Addressing the challenge: Each worker should undergo completer training in order to understand the system.
Challenges of CRM:
- Addressing the challenge for Training: Each worker should undergo completer training in order to understand the system...
Explanation of Solution
Enterprise applications taking advantage of SOA, cloud computing and open sourced software:
Service Oriented Architecture (SOA):
Supple communication between distributed applications in possible because of various standards like HTTP, JavaScript, etc. and it is known as Service oriented architecture.
Cloud computing:
It is a technology that provides various services of Information technology from which the resources are fetched from Internet via web based tools or applications.
Open sourced software:
It is a software which is available for everyone. It can also be modified by the user or other developers...
Explanation of Solution
Social CRM: Social CRM tools allow a business to link conversations of their customers and their interactions from social networking sites to the CRM processes than having them in separate silos.
CRM using social networking:
Social network:
- Social network is a website or social club which allows people to interact with each other by sharing their interests, views, and events.
- It is a network that helps to keep touch with their friends, family, and people...

Trending nowThis is a popular solution!

- Help! how do I fix my python coding question for this? (my code also provided)arrow_forwardNeed help with coding in this in python!arrow_forwardIn 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_forward
- Please 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_forwardHere 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_forward
- 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)…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_forwardThe 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 (missing values vs. missing labels), they should not be combined in the same diagram. Instead, create two separate diagrams for clarity. For reference, I will attach a second image…arrow_forward
- Principles of Information Systems (MindTap Course...Computer ScienceISBN:9781285867168Author:Ralph Stair, George ReynoldsPublisher:Cengage LearningPrinciples of Information Systems (MindTap Course...Computer ScienceISBN:9781305971776Author:Ralph Stair, George ReynoldsPublisher:Cengage Learning
- Fundamentals of Information SystemsComputer ScienceISBN:9781337097536Author:Ralph Stair, George ReynoldsPublisher:Cengage LearningFundamentals of Information SystemsComputer ScienceISBN:9781305082168Author:Ralph Stair, George ReynoldsPublisher:Cengage LearningSystems ArchitectureComputer ScienceISBN:9781305080195Author:Stephen D. BurdPublisher:Cengage Learning




