FellowBuddy.com is a platform which has been setup with a simple vision, keeping in mind the dynamic requirements of students.
Our Vision & Mission - Simplifying Students Life
Our Belief - “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom-446240585585480
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
Sign Language Recognition based on Hands symbols ClassificationTriloki Gupta
Communication is always having a great impact in every domain and how it is considered the meaning of the thoughts and expressions that attract the researchers to bridge this gap for every living being.
The objective of this project is to identify the symbolic expression through images so that the communication gap between a normal and hearing impaired person can be easily bridged.
Github Link:https://github.com/TrilokiDA/Hand_Sign_Language
This document discusses inference in first-order logic. It defines sound and complete inference and introduces substitution. It then discusses propositional vs first-order inference and introduces universal and existential quantifiers. The key techniques of first-order inference are unification, which finds substitutions to make logical expressions identical, and forward chaining inference, which applies rules like modus ponens to iteratively derive new facts from a knowledge base.
This document outlines greedy algorithms, their characteristics, and examples of their use. Greedy algorithms make locally optimal choices at each step in the hopes of finding a global optimum. They are simple to implement and fast, but may not always reach the true optimal solution. Examples discussed include coin changing, traveling salesman, minimum spanning trees using Kruskal's and Prim's algorithms, and Huffman coding.
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
Sign Language Recognition based on Hands symbols ClassificationTriloki Gupta
Communication is always having a great impact in every domain and how it is considered the meaning of the thoughts and expressions that attract the researchers to bridge this gap for every living being.
The objective of this project is to identify the symbolic expression through images so that the communication gap between a normal and hearing impaired person can be easily bridged.
Github Link:https://github.com/TrilokiDA/Hand_Sign_Language
This document discusses inference in first-order logic. It defines sound and complete inference and introduces substitution. It then discusses propositional vs first-order inference and introduces universal and existential quantifiers. The key techniques of first-order inference are unification, which finds substitutions to make logical expressions identical, and forward chaining inference, which applies rules like modus ponens to iteratively derive new facts from a knowledge base.
This document outlines greedy algorithms, their characteristics, and examples of their use. Greedy algorithms make locally optimal choices at each step in the hopes of finding a global optimum. They are simple to implement and fast, but may not always reach the true optimal solution. Examples discussed include coin changing, traveling salesman, minimum spanning trees using Kruskal's and Prim's algorithms, and Huffman coding.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
This document provides an overview of different agent architectures, including reactive, deliberative, and hybrid architectures. It discusses key concepts like the types of environments agents can operate in, including accessible vs inaccessible, deterministic vs non-deterministic, episodic vs non-episodic, and static vs dynamic environments. Reactive architectures are focused on fast reactions to environmental changes with minimal internal representation and computation. Deliberative architectures emphasize long-term planning and goal-driven behavior using symbolic representations. Rodney Brooks proposed that intelligence can emerge from the interaction of simple agents following stimulus-response rules, without complex internal models, as seen in ant colonies.
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
Pre-Defense CSE Thesis Presentation in BAUSTNaiyan Noor
he Pre-Defense meeting serves as a “dress rehearsal” for the Final Defense presentation and is the opportune time to address any final edits, questions, or concerns leading up to the Final Defense.
Know the format of your thesis defence. ...
Prepare and practice your presentation. ...
The dreaded “awkward question” ...
When you don't know the answer… ...
Core content. ...
Dealing with nerves.
The document discusses AND/OR graphs, which are a type of graph or tree used to represent solutions to problems that can be decomposed into smaller subproblems. AND/OR graphs have nodes that represent goals or states, with successors labeled as either AND or OR branches. AND branches signify subgoals that must all be achieved to satisfy the parent goal, while OR branches indicate alternative subgoals that could achieve the parent goal. The graph helps model how decomposed subproblems relate and their solutions combine to solve the overall problem.
The document discusses the K-nearest neighbors (KNN) algorithm, a supervised machine learning classification method. KNN classifies new data based on the labels of the k nearest training samples in feature space. It can be used for both classification and regression problems, though it is mainly used for classification. The algorithm works by finding the k closest samples in the training data to the new sample and predicting the label based on a majority vote of the k neighbors' labels.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
The document discusses four common mechanisms in the Unified Modeling Language (UML): (i) specifications which provide textual definitions for graphical notations, (ii) adornments like notes that attach constraints to elements, (iii) common divisions between classes and objects, and (iv) extensibility mechanisms that allow customizing UML through stereotypes, tagged values, and constraints.
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Analogical reasoning is a powerful learning tool that involves abstracting structural similarities between problems to apply solutions from known problems to new ones. The process involves developing mappings between instances and retrieving, reusing, revising, and retaining experiences. Transformational analogy transforms a previous solution by making substitutions for the new problem, while derivational analogy considers the detailed problem-solving histories to apply analogies.
Recurrent neural networks (RNNs) are a type of artificial neural network that can process sequential data of varying lengths. Unlike traditional neural networks, RNNs maintain an internal state that allows them to exhibit dynamic temporal behavior. RNNs take the output from the previous step and feed it as input to the current step, making the network dependent on information from earlier steps. This makes RNNs well-suited for applications like text generation, machine translation, image captioning, and more. RNNs can remember information for long periods of time but are difficult to train due to issues like vanishing gradients.
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
Artificial Intelligence: Artificial Neural NetworksThe Integral Worm
This document summarizes artificial neural networks (ANN), which were inspired by biological neural networks in the human brain. ANNs consist of interconnected computational units that emulate neurons and pass signals to other units through connections with variable weights. ANNs are arranged in layers and learn by modifying the weights between units based on input and output data to minimize error. Common ANN algorithms include backpropagation for supervised learning to predict outputs from inputs.
FellowBuddy.com is a platform which has been setup with a simple vision, keeping in mind the dynamic requirements of students.
Our Vision & Mission - Simplifying Students Life
Our Belief - “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom-446240585585480
FellowBuddy.com is a platform which has been setup with a simple vision, keeping in mind the dynamic requirements of students.
Our Vision & Mission - Simplifying Students Life
Our Belief - “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom-446240585585480
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
This document provides an overview of different agent architectures, including reactive, deliberative, and hybrid architectures. It discusses key concepts like the types of environments agents can operate in, including accessible vs inaccessible, deterministic vs non-deterministic, episodic vs non-episodic, and static vs dynamic environments. Reactive architectures are focused on fast reactions to environmental changes with minimal internal representation and computation. Deliberative architectures emphasize long-term planning and goal-driven behavior using symbolic representations. Rodney Brooks proposed that intelligence can emerge from the interaction of simple agents following stimulus-response rules, without complex internal models, as seen in ant colonies.
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
Pre-Defense CSE Thesis Presentation in BAUSTNaiyan Noor
he Pre-Defense meeting serves as a “dress rehearsal” for the Final Defense presentation and is the opportune time to address any final edits, questions, or concerns leading up to the Final Defense.
Know the format of your thesis defence. ...
Prepare and practice your presentation. ...
The dreaded “awkward question” ...
When you don't know the answer… ...
Core content. ...
Dealing with nerves.
The document discusses AND/OR graphs, which are a type of graph or tree used to represent solutions to problems that can be decomposed into smaller subproblems. AND/OR graphs have nodes that represent goals or states, with successors labeled as either AND or OR branches. AND branches signify subgoals that must all be achieved to satisfy the parent goal, while OR branches indicate alternative subgoals that could achieve the parent goal. The graph helps model how decomposed subproblems relate and their solutions combine to solve the overall problem.
The document discusses the K-nearest neighbors (KNN) algorithm, a supervised machine learning classification method. KNN classifies new data based on the labels of the k nearest training samples in feature space. It can be used for both classification and regression problems, though it is mainly used for classification. The algorithm works by finding the k closest samples in the training data to the new sample and predicting the label based on a majority vote of the k neighbors' labels.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
The document discusses four common mechanisms in the Unified Modeling Language (UML): (i) specifications which provide textual definitions for graphical notations, (ii) adornments like notes that attach constraints to elements, (iii) common divisions between classes and objects, and (iv) extensibility mechanisms that allow customizing UML through stereotypes, tagged values, and constraints.
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Analogical reasoning is a powerful learning tool that involves abstracting structural similarities between problems to apply solutions from known problems to new ones. The process involves developing mappings between instances and retrieving, reusing, revising, and retaining experiences. Transformational analogy transforms a previous solution by making substitutions for the new problem, while derivational analogy considers the detailed problem-solving histories to apply analogies.
Recurrent neural networks (RNNs) are a type of artificial neural network that can process sequential data of varying lengths. Unlike traditional neural networks, RNNs maintain an internal state that allows them to exhibit dynamic temporal behavior. RNNs take the output from the previous step and feed it as input to the current step, making the network dependent on information from earlier steps. This makes RNNs well-suited for applications like text generation, machine translation, image captioning, and more. RNNs can remember information for long periods of time but are difficult to train due to issues like vanishing gradients.
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
Artificial Intelligence: Artificial Neural NetworksThe Integral Worm
This document summarizes artificial neural networks (ANN), which were inspired by biological neural networks in the human brain. ANNs consist of interconnected computational units that emulate neurons and pass signals to other units through connections with variable weights. ANNs are arranged in layers and learn by modifying the weights between units based on input and output data to minimize error. Common ANN algorithms include backpropagation for supervised learning to predict outputs from inputs.
FellowBuddy.com is a platform which has been setup with a simple vision, keeping in mind the dynamic requirements of students.
Our Vision & Mission - Simplifying Students Life
Our Belief - “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom-446240585585480
FellowBuddy.com is a platform which has been setup with a simple vision, keeping in mind the dynamic requirements of students.
Our Vision & Mission - Simplifying Students Life
Our Belief - “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom-446240585585480
This document contains 200 quantitative aptitude questions divided into different categories such as LCM and HCF, Average, Equations, Time and Work, Partnership, etc. Each section contains 10 questions on the relevant topic with explanations for the answers. The questions range from easy to moderate difficulty levels and test a variety of concepts in mathematics.
This document discusses quantitative techniques (aptitude) and includes the following:
- Aptitude refers to natural talents or abilities for doing certain tasks easily.
- Aptitude includes topics like mathematics, reasoning, percentages, time/work problems, probability, and more.
- Several shortcut methods are provided for train problems and time/speed calculations.
- The document concludes with a series of sample aptitude questions covering various quantitative topics.
The document contains a past MAT exam paper from 2004 containing questions in quantitative ability and verbal ability sections. In the quantitative section, there are 40 multiple choice problems testing concepts like fractions, percentages, ratios, averages, profit/loss, time/work, mixtures, pipes/cisterns, trains, and geometry. In the verbal section, there are 3 multiple choice questions testing word meanings and a passage with 4 blanks to be filled with suitable words. The document provides an old exam paper as a resource for MAT exam preparation.
Interest Assignments
Partnership Assignments
Percentages Assignment
Profit and Loss
Assignments
Proportion Assignments
Set Theory Assignments
Time and Distance Assignments
Time and Work Assignments
Permutation Assignments
Allegation Assignments
AP,GP Assignments
This document appears to be a mathematics post-test from September 2008 consisting of 57 multiple choice questions covering various math topics such as arithmetic, algebra, geometry, ratios, and word problems. The test was administered by the St. Louis Review Center in Manila, Philippines and includes questions testing concepts like operations with fractions and decimals, properties of numbers, measurement conversions, and geometric shapes.
This document contains a 65-item mathematics post-test from April 2010 administered by St. Louis Review Center, Inc. in San Pablo City. The test contains multiple choice, problem solving, and other math questions testing topics like averages, percentages, word problems, operations, and algebraic expressions. It provides the questions, possible answer choices for each, and requests that test takers select the best answer.
ACTUARIAL SCIENCE ACET MODEL PAPER WITH SOLUTION BY SOURAV SIR'S CLASSES 983...SOURAV DAS
The document provides a mock test paper for Actuarial Science (ACET) by Sourav Sir's Classes in Kolkata and New Delhi. It contains 16 multiple choice questions related to percentages, time, speed, distance, ratios, profit/loss, time/work and logarithms. For each question, the correct answer is provided along with a step-by-step explanation. The document aims to help students prepare for the ACET exam through practice questions and detailed explanations of the solutions.
ACTUARIAL SCIENCE | MODEL TEST EXAM | NOTES SOLUTIONS STUDY MATERIAL BY SOURA...SOURAV DAS
The "Actuarial Common Entrance Test" (ACET) is an entrance exam for those who want to join as student members of the Institute of Actuaries of India (IAI) and plan to take & qualify in the various actuarial examinations conducted by the IAI.
It is prepared by Sourav sir's Classes
The document contains 30 questions related to mathematics, logical reasoning, and relationships. The questions cover a range of topics including percentages, ratios, series, arrangements, probabilities, and family relationships. The questions are multiple choice with 4 answer options for each.
This document contains a 25 question GAT (Graduate Aptitude Test) with questions in the areas of logical reasoning, math, English, and general knowledge. The test includes questions that require identifying relationships between words and concepts, finding odd ones out, completing word patterns and series, solving math word problems, and determining analogies.
This document contains a past paper for the MAT exam from May 2008. It includes 47 multiple choice questions across various topics including mathematics, logical reasoning, and general knowledge. The questions cover areas like time, dates, percentages, ratios, geometry, probability, and current events.
The document provides details about a sample test for a Fashion Management program. [1] It outlines 150 multiple choice questions testing various abilities like quantitative ability, English comprehension, analytical ability, and general awareness. [2] The questions are across various subject areas and students must mark their answers on a separate answer sheet using a ballpoint pen to darken the appropriate circle for the best answer choice. [3] The test is aimed at assessing students' suitability for the Fashion Management program.
This document describes the rules and structure of a mental math competition consisting of 5 rounds. It provides sample questions and answers for each round, with the rounds getting more difficult. The competition involves teams answering math problems within time limits, with points awarded for correct answers and deducted for incorrect ones. Teams can optionally pass questions to other teams in some rounds. The final round involves answering multiple questions at once within a time limit.
The document provides a Sitel arithmetic placement paper with 29 multiple choice questions testing arithmetic and age word problems. It also provides information about websites to visit for placement papers, interview tips, job updates and to subscribe to free updates by email or Facebook. The questions cover topics like ratios, averages, proportions, time/work problems and age word problems. The correct answers to each question are highlighted in bold.
This document contains 39 math problems from an unsolved MAT past paper from September 2007. Each problem is multiple choice with 4 possible answers. The problems cover a range of math topics including proportions, time, speed, ratios, percentages, averages, geometry, probability, and more.
This document provides information about Six Phrase - The Finishing School which offers various training programs including Aptitude Training, English Language Training, Soft Skills Training, Technical Training, Finance Training, and Placement Services. It also includes 15 sample aptitude questions related to topics like ratios, mixtures, time and work problems, geometry, percentages, time and distance, and data interpretation.
This document contains 25 multiple choice questions related to mathematics, probability, and logic. The questions cover a range of topics including arithmetic, algebra, geometry, time/work problems, and word problems. Sample questions include finding the minimum number of mice required to identify a poisoned barrel of beer, calculating average speed given a change in distance and time, and determining the number of handshakes that would occur if 20 men shook hands without repetition. The document also provides links to additional resources for frequently asked placement questions.
Similar to Quantitative aptitude (qa1)_www.fellowbuddy.com (20)
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
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Ratio and Proportion, Indices and Logarithm Part 4FellowBuddy.com
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Ratio and Proportion, Indices and Logarithm Part 1FellowBuddy.com
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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For more information about PECB:
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Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
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Quantitative Aptitude
1. Suman borrowed Rs. 60,000 to build a bungalow. She pays 10 % S.I. She lets out her bungalow and
receives a rent of Rs. 1500 p.m. In how many years will she clear her debt.
a. 1 year
b. 4 years
c. 5 years
d. 2 years
2. If the interest on Rs. 800 exceeds that on Rs. 600 by Rs. 15.50 in 6 months, find the rate percent p.a.
a. 15 I/2 %
b. 18 1/2 %
c. 14%
d. 5%
3. Find the compound interest in Rs.2400 for 2 1/2 years at 5 % p.a., interest being compounded
annually.
a. 270.50
b. 300
c. 412.25
d. 312.15
4. A square piece of ground is 45 meters long. Find the cost of fencing it round with 10 lines of wire at
the rate of Rs. 1.50 per meter.
a. 2700
b. 2500
c. 2300
d. 2800
5. The external dimensions of a box are 10 cm, 6 cm ,& 4 cm. If the thickness of wood is 1 cm, find
the volume of wood.
a. 125
b. 150
c. 176
d. 183
6. The radii of two cylinders are in the ratio 2: 3 and heights 5: 3. Calculate ratio of their volumes.
a. 20 /27
b. 27/ 20
c. 3 /4
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d. 20 /32
7. The diameter of a roller. 100 cm long is 60 cm. It takes 500 complete revolutions to level a
playground. What is the cost of leveling @ 50 ps. per sq. m.
a. Rs. 200
b. Rs. 220
c. Rs.400
d. Rs. 500
8. If a+b+c = 0.The value of
a4+b4+c4/b2c2+c2a2+a2b2 is:
a. 2
b. 3
c. 6
d. 8
9. A man divides his property among his sons so that one gets half, another gets quarter and third gets
one fifth and the fourth Rs. 10000. What is the value of the total property?
a. 200000
b. 400000
c. 1400000
d. 2400000
10. If m: n = 2:3, the value of 3m+5n/6m-n is
a. 7/3
b. 3/7
c. 5/3
d. 3/5
11. Divide 27 into two parts so that 5 times the first and eleven times the second are together equal to
195
a. 17:10
b. 15:11
c. 16:10
d. 10:17
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12. 40% of 5/6 is:
a. 1/3
b. 2/3
c. 2/6
d. 3/4
13. A dog taken 4 leaps for every 5 leaps of a hare but three leaps of the dog is equal to 4 leaps of the
hare. Compare their speeds.
a. 16:15
b. 12:16
c. 19:20
d. 10:12
14. 55 men can finish a job in 42 days. If the work has to be completed 9 days earlier, how many
additional men must be engaged to complete the work?
a. 15
b. 20
c. 25
d. 12
15. A retailer buys an electronic buzzer for Rs. 215. His overhead expenses are Rs. 25. If he sells the
buzzer for Rs. 300, his profit percentage would be
a. 25%
b. 26%
c. 20%
d. 30%
16. In an election between two candidates one got 55% of the total valid votes. 20% of the votes are
invalid. If the total votes are 7500, what is the number of valid votes that the other person got?
a. 2700
b. 2800
c. 3000
d. 3300
17. Aunt uses a car to travel from Mumbai to Nasik. She travelled at 60 kmph and covered half the
distance in 2 hours. At what uniform speed should she travel to reach Nasik at 4.00 pm?
a. 40
b. 60
c. 50
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d. 35
18. The sum of ages of Nitya and her aunt is 63 years. Four years ago her aunt's age was 4 times that of
Nitya's ages at that time. What is the present age in years of Nitya's aunt?
a. 48
b. 50
c. 45
d. 40
19. A watch ticks 90 times in 95 secs. And another watch ticks 315 times in 323 secs. If they start
together, how many times will they tick together in first hour?
a. 101 times
b. 100 times
c. 99 times
d. 102 times
20. Area of a square is given to be S. If the sides are doubled, then the new area will be,
a. 4S
b. 2S
c. S/2
d. S2
21. The product of two numbers is 2400. If the bigger number is 1.5 times the smaller number is
a. 40
b. 45
c. 35
d. 30
22. Six men earn as much as 8 women, 2 women earn as much as 3 boys and 4 boys earn as much as 5
girls. If a girl earns Rs.50 a day, then the earning of a man would be
a. Rs.125
b. Rs.135
c. Rs.105
d. Rs.120
23. A train 150 meters long running at a speed of 60 kmph takes 30 seconds to cross a bridge. The length
of the bridge is,
a. 350m
b. 450m
c. 300m
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d. 400m
24. If a sum of money at certain rate of interest doubles in 5 years and at a different rate of interest
becomes 3 times in 12 years, the better rate of interest is,
a. 20%
b. 10%
c. 162/3
d. 15%
25. 126, 756,252,1512, ____
a. 504
b. 500
c. 1210
d. None of these
26. The cost of painting the walls of a hall 30 meters long, 20 meters broad and 12 meters high, if it costs
25 paise per sq. meter.
a. Rs.300
b. Rs.325
c. Rs.350
d. Rs.400
27. The product of 3sqrt (4), 6sqrt (6) and 2sqrt (5) is
a. 6sqrt(12000)
b. 6sqrt(1000)
c. 4sqrt(12000)
d. 3sqrt(120000)
28. If A can complete a work in 6 hours, and B can complete it in 8 hours, then how much time would
they take to complete the work together?
a. 3 3/7
b. 2 5/2
c. 4 5/6
d. none of these.
29. A,B, and C can independently complete a task in 6,8 and 12 hours respectively. C starts working. 2
hours later she stops and simultaneously A and B start working. Yet another 2 hours later, B stops
working too. Now how long does A have to work alone to complete the task?
a. 2 hours
b. 1.5 hours
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c. 3 hours
d. 4 hours
30. 128, 32, 224, 56, ____
a. 392
b. 350
c. 25
d. 36
31. Taps A and B can independently fill a tank in 6 and 10 hours . Leak C can empty a filled tank in 8
hours. On an empty tank A, B, C are opened. How long does it take to fill the tank?
a. 7.05
b. 8.05
c. 10.10
d. 5.07
32. Ten years age, Ram was 2/3 Shyam's age. 10 years hence Shyam will be 25% older than Ram. What
are their present ages?
a. 30;40
b. 40;30
c. 20;40
d. 30;60
33. 25 years ago A's age was 1/3 of what B's age will be 25 years hence 50 years hence, B's age will be
twice A's present age. What is the sum of their present ages?
a. 90
b. 100
c. 200
d. 400
34. The sum of X and Y's age is 105. When X was Y's age, she was 1.5 times Y's age then what are their
present ages?
a. 45;60
b. 30;40
c. 50;60
d. 60;45
35. 10 men working for 20 days, 5 hours a day can build a fence of length 20 meters. I need to build a
fence of length 100 meters in 5 days and the men are willing to work 10 hours a day. How many men do
we need?
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a. 200
b. 300
c. 400
d. 100
36. Find the factor of a4+64
a. (a2+8)(a2-8)
b. (a2+2)(a2-2)
c. (a2+4a+8)(a2-4a+8)
d. None of these.
37. A contractor undertakes to complete a project in 56 days using 100 men. However, after 46 days,
100 men thereby completing the project on time. Had the additional men not been deployed, then by
how many days would the project be delayed?
a. 10
b. 20
c. 5
d. 40
38. Exam-1 = 30/50 Exam2 = 40/50
a) What is the increase in percent?
b) What is the % of increase?
a. 20;33 1/3
b. 33 1/3;20
c. 20;40
d. 40;33 1/3
39. a) What is 30% of 60%?
b) What % is 30% of 60%?
c) What % of 30% is 60%?
a. 17%;45%;190%
b. 18%;50%;200%
c. 20%;100%;150% 5
d. 0%;18%;200%
40. a)The price of sugar increases by 25%. By what % should consumption be decreased so that
expenditure on sugar remains constant?
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b) Consider the same problem but price increases by 50%?
a. 40;66.66
b. 80;99.99
c. 20;33.33
d. 10;11.11
41. I have bought a car for some amount. I sell it to Mr.X at 10% loss due to need of money urgently. But
later I buy it again from him at my original purchase price.
a)What is Mr.X's profit?
b) What is my loss?
a. 11.11%;10%
b. 10%;11.11%
c. 20%;22.22%
d. 40%;44.44%
42. A farmer purchases 2 horses for the same price. One he sells at a 10% profit and the other he sells at
a 10% loss. What is his overall profit or loss?
a. Rs.200 loss
b. Rs.200 profit
c. no loss, no profit
d. Rs.400 loss
43. A farmer sells 2 horses for the same price. In one he gains 20% and in the other he loses 20%. What
is his overall profit or loss?
a. 4% profit
b. 4% loss
c. 2% loss
d. 2% profit
44. Money doubles in 3 years. When does it become 8 times?
a. 24 years
b. 12 years
c. 6 years
d. 18 years
45. A magician can double the no. of coins given to her in 1 minute. I give her a certain no. of coins and
after an hour she has a boxful of coins. When was the box 1/4 filled?
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a. 15 min
b. 30 min
c. 58 min
d. 45 min
46. Principal Rs. 100, rate of interest 10%, compound interest, duration 2 years.
a)Compounded annually
b)Compounded daily
a. 121
b. 120
c. 100
d. 12.1
47. I buy a 20% stock of face value 50/- at 45/- and sell it at 55/-. A year later, what is my yield?
a. 45.44%
b. 44.45%
c. 54.55%
d. 55.54%
48. A dishonest seller pretends to sell his goods at cost price, but he uses a weight of 950gm for the kg
weight. Find his gain%
a. 5
b. 6
c. 7
d. 4.5
49. P lent Rs.600 to Q for 2 years and Rs.150 to R for 4 years and received altogether from both Rs.90 as
interest. Find the rate of interest, simple interest being calculated.
a. 1%
b. 5%
c. 6%
d. 3.5%
50. P owes Q Rs.455 payable 4 months hence and Q owes P Rs.450 payable 2 months hence. If they
agree to settle their account what sum should be paid over and to whom reckoning the rate of true
discount p.a.
a. Rs.12
b. Rs.13
c. Rs.11
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d. Rs.16
QUANTITATIVE APTITUDE: EXPLANATORY ANSWERS
1. S.I = 60,000 * 10 /100
= Rs. 6000
Rent received = 1500 * 12
= 18000
Therefore debt cleared in one year = 18000 - 6000
=12000
Therefore in 5 years.
2. 800 - 600 = 200
n = 6 months. = 1 /2 year
r = 100 I /Pn
= 100 * 15.50/ 200 * 1/2
= 1550 / 100
= 15.5%
3. A = P ( 1 + r/ 1002 ( 1 + r /100)
= 2400 ( 1+ 5 /1002 ( 1 + 2 1/2/100)
= Rs. 2712.15
Therefore C. I. = 2712.15 -150; 2400
= Rs. 312.15
4. Perimeter of square = 4a = 4 * 45
=180 m
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Length of fence = 10 * 180
=1800m. with 10 lines of wire.
Therefore cost of wire = 1.50 * 1800
= Rs. 2700
5. External volume = l * b * h
= 10 * 6 * 4
= 240 cm3
Internal dimensions = ( 10 -2); ( 6- 2); ( 4- 2)
= 8 , 4 , 2 cm.
Internal volume = 8 * 4 * 2
= 64 cm3
Therefore volume of cube = 240 - 64
= 176 cm3
6. Let radii --> 2x and 3x
Let height --> 5y and 3y
V1 /V 2= ( 22 /7 ) ( 2x ) 2 ( 5 y ) / ( 22 /7 ) ( 3x ) 2 ( 3y )
= 20 / 27
7. Diameter of a roller = 60 cm
r = 30 cm = 0.30 m
Height of roller = 1.00 m
Curved surface area = 2 ( 22 / 7 ) rh
= 2 * ( 22 / 7 ) * 0.30 * 1
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= 1.32 / 7
= 0.88
Area covered by roller = 500 * 0.88
= 88 * 5
= 440 cm2
Therefore cost of levelling = 0.50 * 440
=5 * 44
= 220
8. Since a+b+c = 0
a+b = -c
(a+b2=(-c2)
a2+2ab+b2=c2
a2+b2-c2= -2ab
(a2+b2+c2) 2=(-2ab) 2
or
a4+b4+c4+2a2b2-2b2c2-2c
2a2=4a2b2
or
a4+b4+c4=2a2b2+2b2c2+2c2a2
or
=2(a2b2+b2c2+c2a2)
a4+b4+c4/a2b2+b2c2+c2a2
= 2
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9. Let x be the total value of the property.
x = x/2+x/4+1/5x+10000
= (10+5+4)x/20+10000
= 19x/20+10000
= 20*10000
= 200000
10. m/n = 2/3
3m = 2n
now, 3m+5n/6m-n = 2n+5n/4n-n = 7n/3n = 7/3
11. a+b = 27
5a+11b = 195
solving simultaneously we have,
a = 17 b = 10
12. 40% of 5/6 = 40/100*5/6 = 1/3
13. 3 leaps of the dog = 4 leaps of the hare.
1 leap of the dog = 4/3 leaps of the hare.
4 leaps of the dog = 16/3 leaps of the hare.
Speed of the dog : Speed of the hare
16: 15
14. 9 days earlier mean that the work should be completed in (42-9) = 33 days.
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No. of Men = 42*55/33 = 70
Hence the no. of extra men = 70-55
=15 men
15. Total cost of the electronic buzzer = Rs.215+Rs.25 = Rs.240
Selling Price = Rs.300
Profit% = 60/240*100 = 25%
16. 7500*20%=1500
No of valid votes=6000
45% of 6000=2700
Hence[1]
17. At the speed of 60 kmph, aunt will reach nashik in 4 hours.
The distance between mumbai and nashik is 240 kms.
If 120 km are to be covered in 3 hours then aunt should travel at the uniform speed of
120/3 = 40kmph
18. If x and y are the ages of Nitya and Nitya's aunt,
x+y = 63
y-x = 4(x-4)
we have Nitya's age = 15 years
her aunt's age = 48 years
19. First watch ticks after 95/90 seconds.
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second watch after 323/315 seconds.
LCM = 19*5*17/45 seconds.
No. of times they will tick in first 3600 seconds = 3600/(19*5*17)/45
Once they have ticked together in the begining , 50 in 1 hour they tick 100+1 = 101 times
20. If area = S, Sides = sqrt(S)
If side is doubled i.e. 2sqrt(S)
area = {2sqrt(S)}2
= 4S
21. Let a and b be the numbers where b is the bigger number.
b =3/2a
a-b = 2400(given)
3/2a2= 2400
a2= 1600
a = 40
22. 6 M = 8W, 2W = 3B, 4B = 5G
1 G = Rs. 50 a day.
Now, 1B = 5/4G 1 W = 3/2 * 5/4G
1 M= 8/6* 3/2* 5/4G = 5/2G
1 M = 5/2 * 50 = 125/-Rs.
23. The distance covered by the train in 30 seconds = 6000/3600* 30 metres
= 500 metres.
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Length of the Bridge = 500 - 150
= 350m.
24. If we assume that the sum = Rs. 100
Then, 1st rate of interest = 100*100/100*5 = 20
2nd rate of interest = 200*100/100*12 = 16 2/3
76n1=5, x1= 42.2 n2 = 4, x2 = 35.5
x12 = n1x1+ n2x2 / n1+n2
= 5 * 42.2 + 4 * 35.5 /9
= 211.0 + 142.0 /9
= 353 /9
= 39 2/9
25.*6, /3
Hence[1]
26. Area of the four walls.
= 2(length + breadth ) * height
= 2 (30 + 20 ) *12 sq. meteres
= 1200 sq. metres
Total cost of painting = Rs. 1200*25/100
= Rs.300
27.Product of 3 sqrt (4), 6 sqrt (6), 2 sqrt (5) is
4 1/3 * 6 1/6 *5 1/2
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=42/6 * 61/6 * 53/6=(42 * 6 * 53)1/6
=( 16 *6 *125)1/6
= 6 sqrt.12000
28. A takes 6 hours to complete a work.
He can complete 1/6th of the work in one hour.(taking complete work as 1.)
B takes 8 hours to complete it.
He can complete 1/8th of the work in one hour.
Together, A and B can complete,
1/6+1/8 = 14/48 = 7/24 of the work.
Hour Work
1 7/24
? 24/24
=24/7 = 3 3/7
Hence, [1]
29. In one hour, C can complete 1/12th of the work.
In two hours, he can complete 1/12*2 = 2/12th of the work.
A and B start work simultaneously,
together work completed by A and B,
1/6+1/8 = 14/48 work = 7/24 work.
In 2 hours they complete 7/24*2 = 7/12 work
Total work done by A,B,C = 7/12+2/12 = 9/12
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Total work to be completed = 12/12 or 1
Remaining work = 3/12
Hour work done by A
1 1/6
? 3/12
3/12*6 = 1.5 hours
Hence[2]
30./4, *7
Hence[1]
31. In 1 hour C can empty 1/8th of tank
In 1 hour amount of water filled in the tank = A+B-C
= 1/6+1/10-1/8
68/480 = 17/120
Hour Water filled in tank
1 17/120
? 120/120
= 120/17 = 7.05 hours
32. Let the present ages of Ram and Shyam be R and S resp.
3R-2S = 10 -- I
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4S-5R = 10 -- II
15R-10S = 50
-15R+12S = 30
2S = 80
S = 40
R = 30
Hence,[1]
33. Let their present ages be A and B.
3A-B = 100 -- I
2A-B = 50 -- II
3A-B = 100
-2A-B = 50
A = 50
B = 50
Sum of their ages = A+B
=100
Hence[2]
34. Let their present ages be X and Y.
X+Y = 105 --I
1.5X-2Y = 0 --II
X = 60
Y = 45
Hence[4]
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35. M D H L
old 10 20 5 20
new x 5 10 100
10/x = 5/20*10/5*20/100
x = 100
36. a4+b4 = (a2+ab+b2)(a2-ab+b2)
=(a2+4a+8)(a2-4a+8)
=(a2+8)2-(4a)2
=a2+64
Hence[3]
37. x = 10*2 = 20 days
100 men would require 20 days from 46 days
46+20 = 66 days
But we want to complete the work in 56 days.
Hence[1]
38. Exam 1 = 30/50*100 = 60%
Exam 2 = 40/50*100 = 80%
Increase in percent =20%
percent in increase = 20/60*100 = 33 1/3%
39. a)30/100*60/100 = 18/100 = 18%
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b)30% is 50% of 60%
c)60% is 200% of 30%
40. a)Old price of sugar = Rs.10/-
New price of sugar = Rs.12.5/-
Old consumption = (10*100) = 1000
New consumption = (12.5*x)= 1000
x = 1000/12.5 = Rs.80/-
Decrease in consumption = 20/100*100 = 20%
b) old price = Rs.10
New price=Rs.15
10*100 = 1000
15*x = 1000
x = 1000/15 = 66.66Rs.
Decrease in consumption = 33.33%
41. a)Let my cost price be 100
Selling price = 90
Profit for Mr. X = 10
Profit % = 10/90*100 = 11.11%
b) Let cost price be 100
Selling Price = 900
My loss = 10 = 10/100*100 = 10%
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42. Let cost price be 100 of both horses.
horse1 = S.P. = 110
horse 2 = S.P.= 90
Total cost price = 100+100=200
total selling price= 110+90 = 200
Hence[3]
43. Horse 1- C.P S.P.
x 100
? 120
S.P. = C.P.(100+profit%/100)
x = 83.33
Horse 2- C.P S.P.
y 100
? 80
y = 125
Total cost price = cost price of Horse1+cost price of Horse2
= 125 + 83.33 = 208.33
Total S.P.= 100+100 = 200
loss = 8.33
% of loss = 4%
Hence[2]
44. 3 years - 2 times
6 years - 4 times
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9 years - 6 times
12 years - 8 times
45. In 60 min - box is full.
In 59 min - box is half filled.
In 58 min - box is 1/4 filled.
hence[3]
2
46. A = 100(1+10/100)
= 100(110/100*110/100)
= 121
47 . Income = Rs.10/-
20%*50 = 10
capital gain = 55-45 = 10
gain = 10+10 = 20
% gain = 20/45*100 = 44.45%
48. Suppose goods cost him Rs.1/kg, then he sells goods costing Rs. 0.95 for Rs.1/-
gain = 1-0.95 = 0.05
% gain = 0.05/0.95*100 = 500/95 = 5%
Hence[1]
49. Let r be the rate of interest.
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600*2*r/100 + 150*4*r/100 = 90
1200r/100 + 600r/100 = 90
r = 5
Hence[2]
50. p = Rs.455, n = 4months = 1/3 year, rate = 5%
Amount of Rs.100 = (455*1/3*5)/100 = 107.58
455 ?
107.58 100
= 423
q = Rs.450, n = 2months = 1/6 year, rate = 5%
Amount of Rs.100 = (450*1/6*5)/100 = 103.75
450 ?
103.75 100
= 434
Sum to be repaid to p = 434-423 = Rs.11
Hence [3]