Briefly describe a sign of overfitting in Naive Bayes learning- and ho.docx
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Briefly describe a sign of overfitting in Naive Bayes learning, and how it can be avoided. Solution Briefly, with the Naive Bayes (NB) algorithm the \'naive\' conditional independence assumption means that interactions between variables can be ignored. What follows is: i) it has a simpler hypothesis function (compared with other algorithms e.g. logistic regression) ii) since the interactions are not modeled, some of the information in the data is ignored. This makes it an inherently high bias model; it has a high approximation error but as a result it also does not overfit. (A model with high variance attempts to model all of the data including the noise in the data). iii) Since the interactions are not modeled, less training data is needed. This is why the NB classifier is known to perform well both with small data sets and with missing data. Hereis a small experiment I did to see effect missing data and training data size have on the NB classifier. .
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C++ program
Revising the Array-Based List ADT
Given the data structure
typedef char *Element;
struct List {
Element *data;
int count;
int capacity;
};
Modify the following insertTail operations, instead of returning false when the array is full, the function should attempt to double the capacity of the list and the old array list is copied into the new array list, and then insert new element. Given the following implementation to modify:
bool insertTail(List* l, Element e) {
if (fullList(l)) {
return false;
} else {
l->data[l->count] = e;
l->count++;
return true;
}
}
Solution
Creating tempElement array.. and copy old elements. Then replace old array with new temp Array. And also we need to update capacity of list.
bool insertTail(List* l, Element e) {
if (fullList(l)) {
// temp list declared with double size
int tempCount = l->count;
Element * temp = new Element[2*tempCount];
// copying old list to new list
for(int i=0; i<tempCount; i++){
temp[i] = list->data[i];
}
//now referencing old array with new array
l->data = temp;
l->capacity = 2*tempCount;
}
// no else condition.. it will continue to insert
l->data[l->count] = e;
l->count++;
return true;
}
.
C++ help!! Im not familiar with how to save- read file so that the pro.docx
C++ help!! Im not familiar with how to save/ read file so that the program works so i\'d realllyy appreaciate it if you wrote down the steps along with the code!
Solution
#include<fstream>
#include<iostream>
using namespace std;
int main()
{
int i,j;
int a[4][4],trans[4][4];
ifstream infile(\"Matrix_01.txt\");//change the file name to test for matrix_02 it must be in the same folder
if(!infile)
{
cout<<\"Cannot open file1.\ \";
return
}
for(i=0;i<4;i++)//reads from the file
{
for(j=0;j<4;j++)
{
infile1>>a[i][j];
}
}
cout<<\"Matrix:\"<<endl;//displays the matrix
for(i=0; i<4; ++i)
{
for(j=0; j<4; ++j)
{
cout << \" \" << a[i][j];
}
cout<<endl;
}
cout<<\"Transpose:\"<<endl;
for(i=0; i<4; ++i)//calculates transpose
for(j=0; j<4; ++j)
{
trans[j][i]=a[i][j];
}
for(i=0; i<4; ++i)//prints transpose
{
for(j=0; j<4; ++j)
{
cout << \" \" << trans[i][j];
}
cout<<endl;
}
cout<<\"Upper Triangle:\ \";
for (int i = 0; i < 4;i++)//prints upper traingle
{
for (int j = 0; j < (4- i); j++)
{
cout << a[i][j] << \" \";
}
cout << \"\ \";
}
cout<<\"Lower Triangle:\ \";
for(i=0;i<4;i++)//prints lower triangle
{
for(j=0;j<=i;j++)
{
cout<<a[i][j]<<\" \";
}
cout<<\"\ \";
}
for ( i = 0 ; i < 4 ; i++ )//checks for transpose
{
for ( j = 0 ; j < 4 ; j++ )
{
if ( a[i][j] != trans[i][j] )
break;
}
if ( j != 4 )
break;
}
if ( i == 4 )
cout<<\"It is a Symmetric matrix.\ \";
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cout<<\"It is not a Symmetric matrix.\ \";
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Regularization is used to prevent overfitting by adding a penalty term to the loss function during model training. It discourages complex models and promotes simpler ones, ultimately improving generalization performance.
What is the difference between precision and recall?
Precision is the ratio of true positives to the total predicted positives, while recall is the ratio of true positives to the total actual positives. Precision measures the accuracy of positive predictions, whereas recall measures the coverage of positive instances.
Explain the term “bias-variance tradeoff.”
The bias-variance tradeoff refers to the relationship between a model’s bias (error due to oversimplification) and variance (error due to sensitivity to fluctuations in the training data). Increasing model complexity reduces bias but increases variance, and vice versa. The goal is to find the right balance that minimizes overall error.
notes as .ppt
This document provides an introduction to machine learning, including definitions, examples of tasks well-suited to machine learning, and different types of machine learning problems. It discusses how machine learning algorithms learn from examples to produce a program or model, and contrasts this with hand-coding programs. It also briefly covers supervised vs. unsupervised vs. reinforcement learning, hypothesis spaces, regularization, validation sets, Bayesian learning, and maximum likelihood learning.
Adaptive Classification of Imbalanced Data using ANN with Particle of Swarm O...
Customary characterization calculations can be constrained in their execution on exceedingly uneven informational collections. A famous stream of work for countering the substance of class inelegance has been the use of an assorted of inspecting methodologies. In this correspondence, we center on planning alterations neural system to properly handle the issue of class irregularity. We consolidate distinctive rebalance heuristics in ANN demonstrating, including cost delicate learning, and over and under testing. These ANN based systems are contrasted and different best in class approaches on an assortment of informational collections by utilizing different measurements, including G mean, region under the collector working trademark curve, F measure, and region under the exactness review curve. Numerous regular strategies, which can be classified into testing, cost delicate, or gathering, incorporate heuristic and task subordinate procedures. So as to accomplish a superior arrangement execution by detailing without heuristics and errand reliance, presently propose RBF based Network RBF NN . Its target work is the symphonious mean of different assessment criteria got from a perplexity grid, such criteria as affectability, positive prescient esteem, and others for negatives. This target capacity and its enhancement are reliably detailed on the system of CM KLOGR, in light of least characterization mistake and summed up probabilistic plunge MCE GPD learning. Because of the benefits of the consonant mean, CM KLOGR, and MCE GPD, RBF NN improves the multifaceted exhibitions in a very much adjusted way. It shows the definition of RBF NN and its adequacy through trials that nearly assessed RBF NN utilizing benchmark imbalanced datasets. Nitesh Kumar | Dr. Shailja Sharma "Adaptive Classification of Imbalanced Data using ANN with Particle of Swarm Optimization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25255.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/25255/adaptive-classification-of-imbalanced-data-using-ann-with-particle-of-swarm-optimization/nitesh-kumar
A SURVEY OF METHODS FOR HANDLING DISK DATA IMBALANCE
Class imbalance exists in many classification problems, and since the data is designed for accuracy,
imbalance in data classes can lead to classification challenges with a few classes having higher
misclassification costs. The Backblaze dataset, a widely used dataset related to hard discs, has a small
amount of failure data and a large amount of health data, which exhibits a serious class imbalance. This
paper provides a comprehensive overview of research in the field of imbalanced data classification. The
discussion is organized into three main aspects: data-level methods, algorithmic-level methods, and hybrid
methods. For each type of method, we summarize and analyze the existing problems, algorithmic ideas,
strengths, and weaknesses. Additionally, the challenges of unbalanced data classification are discussed,
along with strategies to address them. It is convenient for researchers to choose the appropriate method
according to their needs.
Issues in DTL.pptx
The document discusses several practical issues in learning decision trees: 1) determining the depth to grow the tree to avoid overfitting, 2) handling continuous attributes, 3) choosing an appropriate attribute selection measure, 4) handling missing attribute values, and 5) handling attributes with differing costs. It also discusses techniques for avoiding overfitting like pre-pruning and post-pruning trees as well as reduced error pruning and rule post-pruning.
SPSS GuideAssessing Normality, Handling Missing Data, and Calculating Scores...
"This comprehensive SPSS guide covers essential topics in data analysis and statistical research. Key contents include:
Missing Data: Understanding and handling data gaps (Page 2)
Assessing Normality: Why and how to check normality in data sets (Page 6)
Interpretation of Output: A guide to exploring and interpreting SPSS outputs (Page 8)
Skewness and Kurtosis: Insights into data distribution (Page 11)
Kolmogorov-Smirnov and Shapiro-Wilk Tests: Testing for normality (Page 14)
Manipulating Data: Techniques and strategies for data manipulation (Page 25)
Calculating Total Scores and Reversing Negative Worded Items: SPSS guidance (Page 26)
Ideal for students, educators, researchers, and professionals in data analysis and statistics."
A ROBUST MISSING VALUE IMPUTATION METHOD MIFOIMPUTE FOR INCOMPLETE MOLECULAR ...
This document presents a new method called MiFoImpute for imputing missing values in molecular descriptor datasets. MiFoImpute uses an iterative random forest approach. It is compared to 10 other imputation methods on two molecular descriptor datasets with varying percentages of artificially introduced missing values (10-30%). Experimental results show that MiFoImpute has competitive or better performance than other methods according to NRMSE and NMAE error metrics. It exhibits robustness to increasing levels of missing data and computational efficiency compared to some other methods.
renewed-poster-presentation (12)
This document discusses a study that used classificatory decomposition and multi-objective evolutionary algorithms to classify protein LIBS spectra. Classificatory decomposition breaks down LIBS spectra into basis functions and coefficients to reconstruct the original signals while minimizing reconstruction error and classification error. The researchers used a leave-one-out approach where a single LIBS instance is left out of training and used to test the classification scheme, which is repeated for all instances. Their results found an average reconstruction error of around 0.2 and an 80% reduction, and their classification scheme performed as either correct or incorrect.
SELECTED DATA PREPARATION METHODS
This document discusses several methods for preparing data before analysis, including handling outliers, missing data, duplicated data, and heterogeneous data formats. For outliers, it describes techniques like trimming, winsorizing, and changing regression models. For missing data, it covers identifying patterns, assessing causes, and handling techniques like listwise deletion, imputation, and multiple imputations. It also addresses detecting and removing duplicate records based on field similarities, as well as standardizing heterogeneous data formats.
Dealing with imbalanced data sets.pdf
This document discusses addressing imbalanced data sets where the distribution of classes is unequal. Imbalanced data can cause models to be biased towards the majority class and perform poorly on the minority class. Various techniques are presented to address this issue including sampling methods like oversampling and undersampling, cost-sensitive learning, and ensemble methods. Two case studies on credit card fraud detection and medical diagnosis with imbalanced data sets are also summarized where combinations of these techniques improved model performance.
COMPARISION OF PERCENTAGE ERROR BY USING IMPUTATION METHOD ON MID TERM EXAMIN...
The issue of incomplete data exists across the enti re field of data mining. In this paper,Mean Imputation,Median Imputation and Standard Dev iation Imputation are used to deal with challenges of incomplete data on classifi cation problems. By using different imputation methods converts incomplete dataset in t o the complete dataset. On complete dataset by applying the suitable Imputatio n Method and comparing the percentage error of Imputation Method and comparing the result
Twala2007.doc
The document compares techniques for handling incomplete data when using decision trees. It investigates the robustness and accuracy of seven popular techniques when applied to different proportions, patterns and mechanisms of missing data in 21 datasets. The techniques include listwise deletion, decision tree single imputation, expectation maximization single imputation, mean/mode single imputation, and multiple imputation. The results suggest important differences between the techniques, with multiple imputation and decision tree single imputation generally performing better than the others. The choice of technique depends on factors like the amount and nature of the missing data.
An advance extended binomial GLMBoost ensemble method with synthetic minorit...
Classification is an important activity in a variety of domains. Class imbalance problem have reduced the performance of the traditional classification approaches. An imbalance problem arises when mismatched class distributions are discovered among the instances of class of classification datasets. An advance extended binomial GLMBoost (EBGLMBoost) coupled with synthetic minority over-sampling technique (SMOTE) technique is the proposed model in the study to manage imbalance issues. The SMOTE is used to solve the proposed model, ensuring that the target variable's distribution is balanced, whereas the GLMBoost ensemble techniques are built to deal with imbalanced datasets. For the entire experiment, twenty different datasets are used, and support vector machine (SVM), Nu-SVM, bagging, and AdaBoost classification algorithms are used to compare with the suggested method. The model's sensitivity, specificity, geometric mean (G-mean), precision, recall, and F-measure resulted in percentages for training and testing datasets are 99.37, 66.95, 80.81, 99.21, 99.37, 99.29 and 98.61, 54.78, 69.88, 98.77, 96.61, 98.68, respectively. With the help of the Wilcoxon test, it is determined that the proposed technique performed well on unbalanced data. Finally, the proposed solutions are capable of efficiently dealing with the problem of class imbalance.
IRJET- Predicting Customers Churn in Telecom Industry using Centroid Oversamp...
This document proposes a new oversampling technique called Centroid Oversampling to address imbalanced class distributions in customer churn prediction problems. It summarizes existing oversampling methods like SMOTE and introduces Centroid Oversampling, which generates synthetic samples by calculating the centroid of the three nearest data points rather than oversampling outliers. Experimental results on three telecom datasets show Centroid Oversampling achieves better accuracy, recall, and F-measure than SMOTE when used with a KNN classifier, particularly on datasets with high imbalance.
Neural networks, naïve bayes and decision tree machine learning
The document discusses several machine learning algorithms: artificial neural networks, naive Bayes classification, and decision trees. It provides examples of applying these algorithms to classify banking customers and compare their performance. Neural networks had the highest accuracy at 88.92% but the longest processing time of 8.01 seconds. Naive Bayes had the shortest processing time of 0.02 seconds but the lowest accuracy at 86.88%. Decision trees achieved 88.98% accuracy with a processing time of 0.04 seconds. The document also provides real-world examples of applying neural networks to tasks like ECG analysis, credit risk management, and environmental modeling.
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validat...
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validation - Professor Daniel Martin Katz + Professor Michael J Bommarito
Regularization_BY_MOHAMED_ESSAM.pptx
This document discusses various techniques for regularization in machine learning models to prevent overfitting. It defines overfitting and underfitting, and describes ways to detect and address each issue. Specifically, it covers regularization techniques like Lasso regression and Ridge regression that add penalties to reduce the influence of features and complexity. Other techniques discussed include dropout, early stopping, data augmentation, and cross-validation.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
Machine Learning Approaches and its Challenges
Real world data sets considerably is not in a proper manner. They may lead to have incomplete or missing values. Identifying a missed attributes is a challenging task. To impute the missing data, data preprocessing has to be done. Data preprocessing is a data mining process to cleanse the data. Handling missing data is a crucial part in any data mining techniques. Major industries and many real time applications hardly worried about their data. Because loss of data leads the company growth goes down. For example, health care industry has many datas about the patient details. To diagnose the particular patient we need an exact data. If these exist missing attribute values means it is very difficult to retain the datas. Considering the drawback of missing values in the data mining process, many techniques and algorithms were implemented and many of them not so efficient. This paper tends to elaborate the various techniques and machine learning approaches in handling missing attribute values and made a comparative analysis to identify the efficient method.
Anomaly detection via eliminating data redundancy and rectifying data error i...
Anomaly Detection Via Eliminating Data Redundancy and Rectifying Data Error in Uncertain Data Streams
Similar to Briefly describe a sign of overfitting in Naive Bayes learning- and ho.docx (20)
Top 20 Data Science Interview Questions and Answers in 2023.pdf
Top 20 Data Science Interview Questions and Answers in 2023.pdf
Adaptive Classification of Imbalanced Data using ANN with Particle of Swarm O...
Adaptive Classification of Imbalanced Data using ANN with Particle of Swarm O...
A SURVEY OF METHODS FOR HANDLING DISK DATA IMBALANCE
A SURVEY OF METHODS FOR HANDLING DISK DATA IMBALANCE
SPSS GuideAssessing Normality, Handling Missing Data, and Calculating Scores...
SPSS GuideAssessing Normality, Handling Missing Data, and Calculating Scores...
A ROBUST MISSING VALUE IMPUTATION METHOD MIFOIMPUTE FOR INCOMPLETE MOLECULAR ...
A ROBUST MISSING VALUE IMPUTATION METHOD MIFOIMPUTE FOR INCOMPLETE MOLECULAR ...
COMPARISION OF PERCENTAGE ERROR BY USING IMPUTATION METHOD ON MID TERM EXAMIN...
COMPARISION OF PERCENTAGE ERROR BY USING IMPUTATION METHOD ON MID TERM EXAMIN...
An advance extended binomial GLMBoost ensemble method with synthetic minorit...
An advance extended binomial GLMBoost ensemble method with synthetic minorit...
IRJET- Predicting Customers Churn in Telecom Industry using Centroid Oversamp...
IRJET- Predicting Customers Churn in Telecom Industry using Centroid Oversamp...
Neural networks, naïve bayes and decision tree machine learning
Neural networks, naïve bayes and decision tree machine learning
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validat...
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validat...
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...
Anomaly detection via eliminating data redundancy and rectifying data error i...
Anomaly detection via eliminating data redundancy and rectifying data error i...
More from marions12
C++ program Revising the Array-Based List ADT Given the data structure.docx
C++ program
Revising the Array-Based List ADT
Given the data structure
typedef char *Element;
struct List {
Element *data;
int count;
int capacity;
};
Modify the following insertTail operations, instead of returning false when the array is full, the function should attempt to double the capacity of the list and the old array list is copied into the new array list, and then insert new element. Given the following implementation to modify:
bool insertTail(List* l, Element e) {
if (fullList(l)) {
return false;
} else {
l->data[l->count] = e;
l->count++;
return true;
}
}
Solution
Creating tempElement array.. and copy old elements. Then replace old array with new temp Array. And also we need to update capacity of list.
bool insertTail(List* l, Element e) {
if (fullList(l)) {
// temp list declared with double size
int tempCount = l->count;
Element * temp = new Element[2*tempCount];
// copying old list to new list
for(int i=0; i<tempCount; i++){
temp[i] = list->data[i];
}
//now referencing old array with new array
l->data = temp;
l->capacity = 2*tempCount;
}
// no else condition.. it will continue to insert
l->data[l->count] = e;
l->count++;
return true;
}
.
C++ help!! Im not familiar with how to save- read file so that the pro.docx
C++ help!! Im not familiar with how to save/ read file so that the program works so i\'d realllyy appreaciate it if you wrote down the steps along with the code!
Solution
#include<fstream>
#include<iostream>
using namespace std;
int main()
{
int i,j;
int a[4][4],trans[4][4];
ifstream infile(\"Matrix_01.txt\");//change the file name to test for matrix_02 it must be in the same folder
if(!infile)
{
cout<<\"Cannot open file1.\ \";
return
}
for(i=0;i<4;i++)//reads from the file
{
for(j=0;j<4;j++)
{
infile1>>a[i][j];
}
}
cout<<\"Matrix:\"<<endl;//displays the matrix
for(i=0; i<4; ++i)
{
for(j=0; j<4; ++j)
{
cout << \" \" << a[i][j];
}
cout<<endl;
}
cout<<\"Transpose:\"<<endl;
for(i=0; i<4; ++i)//calculates transpose
for(j=0; j<4; ++j)
{
trans[j][i]=a[i][j];
}
for(i=0; i<4; ++i)//prints transpose
{
for(j=0; j<4; ++j)
{
cout << \" \" << trans[i][j];
}
cout<<endl;
}
cout<<\"Upper Triangle:\ \";
for (int i = 0; i < 4;i++)//prints upper traingle
{
for (int j = 0; j < (4- i); j++)
{
cout << a[i][j] << \" \";
}
cout << \"\ \";
}
cout<<\"Lower Triangle:\ \";
for(i=0;i<4;i++)//prints lower triangle
{
for(j=0;j<=i;j++)
{
cout<<a[i][j]<<\" \";
}
cout<<\"\ \";
}
for ( i = 0 ; i < 4 ; i++ )//checks for transpose
{
for ( j = 0 ; j < 4 ; j++ )
{
if ( a[i][j] != trans[i][j] )
break;
}
if ( j != 4 )
break;
}
if ( i == 4 )
cout<<\"It is a Symmetric matrix.\ \";
else
cout<<\"It is not a Symmetric matrix.\ \";
}
.
c++ computer architecture and memory What is fragmentation- Briefly ex.docx
c++ computer architecture and memory
What is fragmentation? Briefly explain what it is and how we address it. Explain the role of the stack and the heap, using an example of calling a function that then allocates memory for 100 integers to a local pointer variable. You may use a diagram if you wish.
Solution
A)Fragmentation is an phenomenon in which using of storage capacity in efficiently
There are three types of fragmentation
1.internal fragmentation
2.external fragmentation
3.data fragmentation
Addressing is in the form offsets,data grand
B)stack is an one of the data structure in which push and pop operations are done
In this first all the elements are push in to stack and then popping out the elementsI
Is done pop()function
Ex:for example I have 100integer variable iam allocating it to stack data structure
main()
{
Int *elements[]; //declaring pointer variabl
Stackelements();
Void stackelements()
{
For(I=0;I<=100;i±+)
Elements[i]=n
}
.
C# There are two schools of thought on the use of implicitly typed var.docx
C#
There are two schools of thought on the use of implicitly typed variables. One school advocates avoiding the use of implicitly types variables in all but a few very specific situations, while another feels it is ok as long as the data type is obvious from the variable declaration. Which camp are you in? Discuss the advantages and dis-advantages of using implicitly typed variables in your applications.
Solution
Dis-advantages over using the implicitly types variable in application:
1. Implicitly-typed variables lose descriptiveness
2. var’ encourages Hungarian Notation
3. Specificity vs. Context
There’s no doubt that variable names must be specific, however, they need never be universally-specific.
Just as a local variable in one method doesn’t need to differentiate itself from variables in other methods
Advantages over using the implicitly types variable in application
1. Increased reliance on IntelliSense
2. No backwards compatibility
.
Brief introduction of the WorldComSolutionWorldCom was started by Bill.docx
Brief introduction of the WorldCom
Solution
WorldCom was started by Bill Fields in Hattiesburg, Mississippi, in 1983 under the name \'Long Distance Discount Services\' (LDDS), providing telecommunication (telecom) services. From the starting day, the services runs profitably. Now, we going to know about big head of Services, Bernie Ebbers (Ebbers) was the take up as company\'s CEO in 1985. Bernie takes up the services to all over the world. Firstly, the company made the number in public trade list in 1989 after that company CEO put concertation on enlarging the company operations by acquiring various companies. Unfoutunaterly, \'Long Distance Discount Services\' (LDDS), LLDS changed its name as WorldCom in In 1995. LDDS survived 12 years good name but World com had only 7 years up to 2002 the company survived with good name. After 2002, WorldCom became to known as MCI after that a massive accounting scandal was taken by company through filing for bankruptcy protection in 2002 and CEO Bernard Ebbers was penalized to 25 years in prison,
.
Brief Exercise 5-5 Crane Corporation has the following accounts includ.docx
Brief Exercise 5-5
Crane Corporation has the following accounts included in its December 31, 2014, trial balance: Equity Investments (trading) $21,000; Goodwill $150,000; Prepaid Insurance $12,000; Patents $220,000; Franchises $130,000.
Prepare the intangible assets section of the balance sheet.
Current AssetsCurrent LiabilitiesIntangible AssetsLong-term InvestmentsLong-term LiabilitiesProperty, Plant and EquipmentStockholders\' EquityTotal AssetsTotal Current AssetsTotal Current LiabilitiesTotal Intangible AssetsTotal InvestmentsTotal LiabilitiesTotal Liabilities and Stockholders\' EquityTotal Long-term LiabilitiesTotal Property, Plant and EquipmentTotal Stockholders\' Equity
Brief Exercise 5-6
Patrick Corporation
Solution
Exercise 5-5
Balance Sheet as at Dec. 31,2014
Exercise 5-6
Exercise 5-9
.
Briefly describe some of the similarities and differences between U-S-.docx
Briefly describe some of the similarities and differences between U.S. GAAP and IFRS with respect to the accounting for investments.
Solution
Similarities 1) An equity investment that gives an investor significant influence over an investee is considered an equity method investment under both US GAAP and IFRS. 2) Further the equity method of accounting for such investments if applicable generally is consistent under both US GAAP and IFRS. 3) Under both US GAAP And IFRS the determination of whether entities are consolidated by a reporting entity is based on control although there is some differences in the definition of control. 4) Further uniform accounting policies are used for all the entities within a consolidated group. Differences 1)Under US GAAP focus is on controlling Financial interests whereas under IFRS focus is on power to control. 2) Under US GAAP preparaion of consolidated financial statements is required in general although certain industry specific exceptions exist. Under IFRS it is also required in general but there is limited exemption from preparing consolidated financial statements for a parent company that is itself a wholly owned subsidiary or is apartially owned subsidiary if certain conditions are met. 3) Under US GAAP potential voting rights are generally not considered in the determination of significant influence whereas under IFRS potential voting rights are considered if currently exercisable in determining significant influence.
.
Briefly describe what the query evaluation engine performs inside a DB.docx
Briefly describe what the query evaluation engine performs inside a DBMS.
Solution
Generally we write queries to retrieve data from database.What we know is just simply submit the query.Actually,so many people don\'t know after submitting query What happend really in between submission of query and retrieving data is: query handeld to query processor .That query processor having different components to process that query
one of the components of query processor is :query evaluation engine :1)it executes the low-level instructions that are generated by DML compiler.DML compiler generate the execution plan with that low-level instructions from the query.
2)query evaluation engine takes input from query optimizer and gives out physical operations.
query optimizer produces best execution plan out of many plans by considering some cost like no of operations,cpu time etc.What actually execution plan consists is logical operators.
These logical operators are taken by query evaluation engine and generate physical operations.
While doing physical operation evaluation engine closely takes into the available resources like memory.
physial operations like sorting,hashing,disk access etc.
.
Briefly explain the purposes of adjustments Briefly explain the purp.docx
Briefly explain the purposes of adjustments
Briefly explain the purposes of adjustments
Solution
The accounting process involves the preparation of adjusting entries. Adjusting journal entries are made to update the accounts and bring them to their correct balances.
The preparation of adjusting entries is an application of the accrual concept of accounting and the matching principle .
The accrual concept states that income is recognized when earned regardless of when collected and expenses are recognized when incurred regardless of when paid.
Also, the matching principle aims to align expenses with revenues. Expenses should be recognized in the period when the revenues earned with them are recognized.
Why Are Adjusting Entries Necessary
The purpose of adjusting entries is to update the accounts to conform with the accrual concept . At the end of the accounting period, some income and expenses may have not been recorded, taken up or updated; hence, there is a need to update the accounts.
If adjusting entries are not prepared, some income, expense, asset, and liability accounts may not reflect their true values when reported in the financial statements. For this reason, adjusting entries are necessary.
BELOW are the types of journal entires.
Generally, there are 4 types of adjusting entries. Adjusting entries are prepared for the following:
.
Budget Martin Corporation granted a nonqualified stock option to e.docx
Budget
Solution
Answer:
Caroline must recognise the following incomes:
1. Long term capital gain of $50,000 as she sold the stock one year later at a price of $300 per share , while she exercised the stoock at a price of $250 per share. [(1000 shares*300)-(1000shares*250)]
2. And Oridinary Income of $100,000 as she exercised the option at the rate of $250 per share while the stok was granted at the rate of $150.[(1000shares*250)-(1000 shares*150)]
.
Calculate fost of goods sold and ending inventory and analyze effect o (1).docx
Calculate fost of goods sold and ending inventory and analyze effect of each method on financial statements. Jefferson company had the following sales and purchases during 2011, its first year of business:
January 5 Purchased 40 units at $100 each
February 15 Sold 15 units at $150 each
April 10 Sold 10 units at $150 each
June 30 Purchased 30 units at $105 each
August 15 Sold 25 units at $150 each
November 28 Purchased 30 untis at $110 each
Solution
Part A: Record the effect of the following transactions on the accounting equation assuming LIFO perpetual:
Transactions in January, 2011:
Assets
Liabilities
Shareholders
.
Calculate fost of goods sold and ending inventory and analyze effect o.docx
Calculate fost of goods sold and ending inventory and analyze effect of each method on financial statements. Jefferson company had the following sales and purchases during 2011, its first year of business:
January 5 Purchased 40 units at $100 each
February 15 Sold 15 units at $150 each
April 10 Sold 10 units at $150 each
June 30 Purchased 30 units at $105 each
August 15 Sold 25 units at $150 each
November 28 Purchased 30 untis at $110 each
Solution
Part A: Record the effect of the following transactions on the accounting equation assuming LIFO perpetual:
Transactions in January, 2011:
Assets
Liabilities
Shareholders
.
Ca(OH)2 is insoluble- What is the total concentration of ions if 2M of.docx
Ca(OH)2 is insoluble. What is the total concentration of ions if 2M of Ca(OH)2 dissolves in water.
Ca(OH)2 is insoluble. What is the total concentration of ions if 2M of Ca(OH)2 dissolves in water.
Solution
Ca(OH)2 is partially soluble in water and we can find solubility based on solubility product
Ca(OH)2 (s) <-----> Ca2+ (aq) + 2OH- (aq)
at equilibrium [Ca2+] = S , [OH-] = 2S
Ksp = [Ca2+] [OH-]^2 ( where Ksp is solubility product of Ca(OH)2 in water)
5.5 x 10^ -6 = (S) (2S)^2
S = 0.0111 M
Thus [Ca2+] = 0.0111 , [OH-] = 2 x 0.0111 = 0.0222 M
Total concetrations of ions = [Ca2+] + 2 [OH-] = ( 0.0111) + 2 ( 0.0222) = 0.0555 M
.
c- Since BEEF hooks to Metasploit- is there any safe way to use the we.docx
c. Since BEEF hooks to Metasploit, is there any safe way to use the web today? Explain.
Solution
a.) The safe way of using web is by implementing firewalls,secure virtual private networks and antivirus.
b.) Implementing the firewalls is an imperative solution for all organizations to protect their sensitive data fro getting lost.Firewall keeps everything secure from the possible attacks.This is done by analysing the data that gets transmitted back and forth in the network.
c.) Implementation is possible through the following steps
- defining the security needs to finf out the level of security necessary.
- researching soutions to match the specified needs
- and finally installing,configuring and maintaining the firewalls.
d.) Virtual private network as the name itself suggest extends a private network across the public network of internet.I tprovides functionality,network management and security benefits to the user.
e.) The antivirus helps in preventing,detecting and removing the malicious software.I tprovides protection from various computer threats phishing attacks,online identity attacks,malicious URL\'s and etc.
.
9- Fill in the following table with names or formulas- as appropriate-.docx
This document is a table that lists the names of various chemical compounds and their chemical formulas. It provides the names for silver sulfate, hydrocyanic acid, gold(III) chlorite, perbromic acid, calcium phosphide, mercury(I) nitrate, sulfurous acid, aluminum chromate, tin(IV) carbonate, and oxalic acid. It also provides the chemical formulas for iron(II) hydrogen carbonate, aqueous nitrous acid, ammonium borate, lithium dichromate, and aqueous hydroiodic acid. The solution explains that the names are based on the type of elements or substituents attached to the compounds.
9- A combination of sand- salt- and water is an example of a A) homoge.docx
9. A combination of sand, salt, and water is an example of a A) homogeneous mixture B) heterogeneous mixture C) compound D) pure substance E) solid 10. Forthe following reaction Chldh +Oz ? CO2 + 11,0 what is the coefficient of oxygen? a. 2 b. 4 c. 6 d. 8 e. None of the above
Solution
9) Salt, sand and water mixture contains two different phases. liquid phase and solid phase.
and moreover they dont mix and form separate phases.
So, this is example of heterogeneous mixture. (B)
10)
Balance the reaction :
C 3 H 4 O2 + O 2 -----------> CO 2 + H 2 O
balance the reaction for Carbon ,
C 3 H 4 O2 + O 2 -----------> 3 CO 2 + H 2 O
balance the reaction for hydrogen,
C 3 H 4 O2 + O 2 -----------> 3 CO 2 + 2 H 2 O
now balance the reaction for oxygen,
C 3 H 4 O2 + 3 O 2 -----------> 3 CO 2 + 2 H 2 O
So, the answer is 3 and option is none of the above.
.
8-6- Which of the highlighted elements in Figure P8-6 has the greatest.docx
8.6. Which of the highlighted elements in Figure P8.6 has the greatest electronegativity? 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 FIGURE P8.6
Solution
Electronegativity is a chemical property of an atom that describes how well the atom withdraws shared pair of electrons towards it. It is measured in terms of Pauling Scale from   0 to 4.
The higher the value, the more the electronegativity of the atom.
The highlighted element 17 (Fluorine, F) has the highest electronegativity value is 3.98 .
The highlighted element 1 (Lithium, Li) has the highest electronegativity value is 0.98.
The highlighted element 2 (Beryllium, Be) has the highest electronegativity value is 1.57.
The highlighted element 13 (Boron, B) has the highest electronegativity value is 2.04.
The highlighted element 14 (Carbon, C) has the highest electronegativity value is 2.55.
The highlighted element 15 (Nitrogen, N) has the highest electronegativity value is 3.04.
The highlighted element 16 (Oxygen, O) has the highest electronegativity value is 3.44.
The highlighted element 18 (Neon, Ne) has the highest electronegativity value is 0.
.
870 The plates of a capacitor are not quite parallel- the distance bet.docx
870 The plates of a capacitor are not quite parallel, the distance between them at the bottom being (D a) and at the top (Dta), where a
Solution
let length of each plate be L and width be W.
then area A=L*W
at a distance x from the bottom end of the plate, distance between the plates as a function of x is given as
d=D-a+(2*a/L)*x
consider a small segment of length dx at a distance of x from the bottom end
area of the segment=W*dx
distance between the two plates=d=(D-a+(2*a/L)*x)
then capacitance=dC=epsilon*area/distance between plates
=epsilon*W*dx/(D-a+(2*a/L)*x)
let D-a+(2*a/L)*x=y
==>(2*a/L)*dx=dy
==>dx=L*dy/(2*a)
substituting,
dC=epsilon*W*L*dy/(2*a*y)
integrating both sides,
C=(epsilon*W*L/(2*a))*ln(y)
as y varies from D-a to D+a
C=(epsilon*W*L/(2*a))*ln((D+a)/(D-a))
as A=W*L
and ln((D+a)/(D-a))
=ln((1+(a/D))/(1-(a/D)))
=2*(a/D)*(1+(a^2/(3*D^2)))
then C=(epslion*A/(2*a))*(2*a/D)*(1+(a^2/(3*D^2)))
=(epsilon*A/D)*(1+(a^2/(3*D^2)))
.
8- What are concurrency design patterns- Explain-Solution8) we have fi.docx
8. What are concurrency design patterns? Explain.
Solution
8)
we have five concurrency design patterns:
-------------------------------------------------
First pattern is Active object design pattern . This pattern decouples method invocation. This is to enhance
concurrency and simplify synchronized access to objects that reside in their own threads of control.
Second pattern is Monitor pattern which synchronizes concurrent method execution to ensure that only one
method at a time runs within an object. It allows obkect methods to schedule their execution sequence.
Third pattern is haof sync / half async pattern which decouples synchrounous and synchronous processing in
concurrent systems, so that it will simplify programmming without reducing performance undudly.
Fourth pattern is Leader pattern which provides concurrency model where multiple threads take turns to share
a set of event sources to detect, demultiplexm dispatch and process service requests that occur on the
event sources.
Fifth pattern is thread specific storage design pattern to allow multiple threads to use one logical global
access point to retrieve an object that is local to a thread, without uncurring locking overload on each access
to the object.
.
8- Calculate the distance from the donor (D) to the acceptor (A) in th.docx
8. Calculate the distance from the donor (D) to the acceptor (A) in the protein represented. Assume that there is a single D-A distance, and that diffusion does not occur during the donor excited-state lifetime. Given:
A.The F DA /F D ratio is 0.50 (that is, the ratio between the fluorescence intensity of the donor in the presence of the acceptor (F DA ) and the fluorescence of the respective control molecule which lacks the acceptor (F D )).
B.The spectral overlap integral  ( J (l)) for this donor-acceptor pair is 4.5 x 10 13 M -1 cm -1 nm 4 .
C.The refractive index of the solution is 1.4.
D.The fluorescence quantum yield of the donor in the absence of acceptor is 0.3.
Solution
Fret E = 1- FDA/FD
E = 1-0.50
= 0.50
Rn = R0 (n(1-E)/E)1/6
Rn is the distance between A and D
E = 0.5
R0 = 4.5 X 1013 unit
Hence Rn = 4.5 X 1013 X 1.4 (1-0.5/0.5)1/6
=4.5 X 1013 X (1.4)1/6
= 4.5 X 1013 X 8.4
= 38291.4 angstrom
N = 1.4
.
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