This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - V [Web Design and Internet Based Applications] (Old Syllabus). [Year - April / 2013] . . . Solution Set of this Paper is Coming soon . . .
NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.
This document discusses project scheduling for software engineering projects. It covers key topics such as:
- The importance of scheduling for establishing a roadmap and tracking progress on large, complex software projects.
- Basic principles of software project scheduling including compartmentalizing work, indicating interdependencies, allocating time and resources, and assigning responsibilities.
- Methods for defining tasks, networks, and timelines to plan and track schedules.
- Techniques for monitoring schedule performance such as status meetings, milestone tracking, and earned value analysis.
- Factors that influence schedules such as risks, changing requirements, estimates, and technical difficulties.
The COCOMO model is a widely used software cost estimation model that predicts development effort and schedule based on project attributes. It includes basic, intermediate, and detailed models of increasing complexity. The intermediate model estimates effort as a function of source lines of code and cost drivers. The detailed model further incorporates the impact of cost drivers on development phases. COCOMO 2 expands on this with application composition, early design, reuse, and post-architecture models for different project stages.
Mini Project final report on " LEAKY BUCKET ALGORITHM "Nikhil Jain
The project “Leaky Bucket Algorithm” is based on computer networks.The leaky bucket algorithm is a general algorithm that can be effectively used to police real time traffic. Both Frame Relay and Aysnchronous Transfer Mode (ATM) networks use a form of the leaky bucket algorithm for traffic management.
For designing this graphical project we require the knowledge of both computer graphics and the language in which it is to be coded.The use of the language helps in designing a package more user friendly since for a general user high end languages creates complexities in understanding and usage.OpenGL provides us with all the inbuilt functions which makes us easy to understand graphics.
Raster scan displays have lower resolution than random scan displays because raster scan stores picture definition as intensity values along scan lines, while random scan stores pictures as line commands. Raster scan directs its electron beam from top to bottom across the entire screen one row at a time, making it less expensive than random scan which only draws required lines. However, random scan produces smoother lines and can provide more realistic displays through advanced techniques like shadows and hidden surfaces.
The Java Stack class implements a last-in, first-out (LIFO) data structure called a stack. It extends the Vector class and inherits its methods. Elements are added to the top of the stack using push() and removed from the top with pop(). The search() method returns the position of an element from the top, and empty() checks if the stack is empty. Common uses of stacks in Java include implementing undo/redo features and evaluating mathematical expressions.
This document discusses different approaches to requirements modeling including scenario-based modeling using use cases and activity diagrams, data modeling using entity-relationship diagrams, and class-based modeling using class-responsibility-collaborator diagrams. Requirements modeling depicts requirements using text and diagrams to help validate requirements from different perspectives and uncover errors, inconsistencies, and omissions. The models focus on what the system needs to do at a high level rather than implementation details.
- Weak slot and filler structures for knowledge representation lack rules, while strong structures like Conceptual Dependency (CD) and scripts overcome this.
- CD represents knowledge as a graphical presentation of high-level events using symbols like actions, objects, modifiers. It facilitates inference and is language independent.
- Scripts represent commonly occurring experiences through structured sequences of roles, props, scenes, and results to predict related events. Both CD and scripts decompose knowledge into primitives for fewer inference rules.
NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.
This document discusses project scheduling for software engineering projects. It covers key topics such as:
- The importance of scheduling for establishing a roadmap and tracking progress on large, complex software projects.
- Basic principles of software project scheduling including compartmentalizing work, indicating interdependencies, allocating time and resources, and assigning responsibilities.
- Methods for defining tasks, networks, and timelines to plan and track schedules.
- Techniques for monitoring schedule performance such as status meetings, milestone tracking, and earned value analysis.
- Factors that influence schedules such as risks, changing requirements, estimates, and technical difficulties.
The COCOMO model is a widely used software cost estimation model that predicts development effort and schedule based on project attributes. It includes basic, intermediate, and detailed models of increasing complexity. The intermediate model estimates effort as a function of source lines of code and cost drivers. The detailed model further incorporates the impact of cost drivers on development phases. COCOMO 2 expands on this with application composition, early design, reuse, and post-architecture models for different project stages.
Mini Project final report on " LEAKY BUCKET ALGORITHM "Nikhil Jain
The project “Leaky Bucket Algorithm” is based on computer networks.The leaky bucket algorithm is a general algorithm that can be effectively used to police real time traffic. Both Frame Relay and Aysnchronous Transfer Mode (ATM) networks use a form of the leaky bucket algorithm for traffic management.
For designing this graphical project we require the knowledge of both computer graphics and the language in which it is to be coded.The use of the language helps in designing a package more user friendly since for a general user high end languages creates complexities in understanding and usage.OpenGL provides us with all the inbuilt functions which makes us easy to understand graphics.
Raster scan displays have lower resolution than random scan displays because raster scan stores picture definition as intensity values along scan lines, while random scan stores pictures as line commands. Raster scan directs its electron beam from top to bottom across the entire screen one row at a time, making it less expensive than random scan which only draws required lines. However, random scan produces smoother lines and can provide more realistic displays through advanced techniques like shadows and hidden surfaces.
The Java Stack class implements a last-in, first-out (LIFO) data structure called a stack. It extends the Vector class and inherits its methods. Elements are added to the top of the stack using push() and removed from the top with pop(). The search() method returns the position of an element from the top, and empty() checks if the stack is empty. Common uses of stacks in Java include implementing undo/redo features and evaluating mathematical expressions.
This document discusses different approaches to requirements modeling including scenario-based modeling using use cases and activity diagrams, data modeling using entity-relationship diagrams, and class-based modeling using class-responsibility-collaborator diagrams. Requirements modeling depicts requirements using text and diagrams to help validate requirements from different perspectives and uncover errors, inconsistencies, and omissions. The models focus on what the system needs to do at a high level rather than implementation details.
- Weak slot and filler structures for knowledge representation lack rules, while strong structures like Conceptual Dependency (CD) and scripts overcome this.
- CD represents knowledge as a graphical presentation of high-level events using symbols like actions, objects, modifiers. It facilitates inference and is language independent.
- Scripts represent commonly occurring experiences through structured sequences of roles, props, scenes, and results to predict related events. Both CD and scripts decompose knowledge into primitives for fewer inference rules.
The document discusses cathode ray tubes (CRTs), which were the primary output devices in graphical systems and video monitors. CRTs display images using an electron beam that scans across phosphorescent screen coatings. There are two types of display methods: raster scan and random scan. Color CRTs use three electron guns and phosphors to produce red, green, and blue light, and require convergence calibration. CRTs were used in televisions, computer monitors, radar displays, and oscilloscopes. They can produce bright, high quality images but also have disadvantages like health hazards from radiation emission and potential overheating issues.
The document discusses various software project size estimation metrics. It describes the limitations of lines of code (LOC) counting, such as variability due to coding style and not accounting for non-coding effort. Function point analysis and feature point analysis are presented as alternatives that overcome some LOC limitations by basing size on software features rather than lines. The key steps of function point analysis involve counting types of inputs, outputs, inquiries and other parameters to calculate unadjusted function points which are then adjusted based on technical complexity factors. While more accurate than LOC, function point analysis is still subjective based on how parameters are defined and counted.
The branch-and-bound method is used to solve optimization problems by traversing a state space tree. It computes a bound at each node to determine if the node is promising. Better approaches traverse nodes breadth-first and choose the most promising node using a bounding heuristic. The traveling salesperson problem is solved using branch-and-bound by finding an initial tour, defining a bounding heuristic as the actual cost plus minimum remaining cost, and expanding promising nodes in best-first order until finding the minimal tour.
closure properties of regular language.pptxThirumoorthy64
1) Regular languages have closure properties where certain operations on regular languages produce new regular languages. These include operations like union, concatenation, and Kleene star.
2) Finite automata have several decidable properties including emptiness, non-emptiness, finiteness, infiniteness, membership, and equality. These properties can be checked through operations on the automata like removing unreachable states.
3) Pushdown automata extend finite automata with a stack. They can be defined by their states, input symbols, stack symbols, transitions, and acceptance conditions like empty stack. Parse trees provide a hierarchical representation of how the symbols in a string derive from a grammar's starting symbol.
Sets are unordered collections of unique elements. Elements within a set cannot be accessed by index since sets are unordered. Common set operations include union, intersection, difference, and symmetric difference. Sets can be created using curly brackets or the set() function. Items can be added and removed from sets using methods like add(), remove(), discard(), and clear(). The length of a set can be determined using len(). Mathematical set relationships like subset, superset, and disjointness can be tested using methods like issubset(), issuperset(), and isdisjoint().
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
The document discusses component-based software engineering and defines a software component. A component is a modular building block defined by interfaces that can be independently deployed. Components are standardized, independent, composable, deployable, and documented. They communicate through interfaces and are designed to achieve reusability. The document outlines characteristics of components and discusses different views of components, including object-oriented, conventional, and process-related views. It also covers topics like component-level design principles, packaging, cohesion, and coupling.
This document discusses Python database programming. It introduces databases and how they store data in tables connected through columns. It discusses SQL for creating, accessing, and manipulating database data. It then discusses how Python supports various databases and database operations. It covers the Python DB-API for providing a standard interface for database programming. It provides examples of connecting to a database, executing queries, and retrieving and inserting data.
Time complexity analysis specifies how the running time of an algorithm depends on the size of its input. It is determined by identifying the basic operation of the algorithm and counting how many times it is executed as the input size increases. The time complexity of an algorithm can be expressed as a function T(n) relating the input size n to the running time. Common time complexities include constant O(1), linear O(n), logarithmic O(log n), and quadratic O(n^2). The order of growth indicates how fast the running time grows as n increases and is used to compare the efficiency of algorithms.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
This document provides an overview of computer graphics and its applications. It discusses various types of video display devices used in computer graphics like CRTs and flat panel displays. It describes how raster scan and random scan systems work and lists common input and output devices. The document outlines different chapters that will cover topics like line and curve generation algorithms, transformations, 3D viewing, surface detection, and modeling techniques. It provides examples of how computer graphics is used in fields like CAD, presentations, entertainment, education, visualization, image processing, and graphical user interfaces.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Tra...Edureka!
This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014. Below are the topics covered in this tutorial:
1. What is Data Analysis?
2. What is Pandas?
3. Pandas Operations
4. Use-case
This document discusses testing off-the-shelf (COTS) components. It defines COTS components as independently developed and reusable parts that are selected from a repository and assembled to build software systems. While COTS components reduce development costs and time, they present challenges to testing due to being treated as black boxes without access to requirements or development processes. The document outlines types of COTS component testing, including black-box testing of inputs/outputs, fault injection to evaluate error handling, operational testing in integrated systems, and interface propagation analysis to observe impacts of faults between components.
This document summarizes Python operators including arithmetic, assignment, comparison, logical, identity, membership, and bitwise operators. It provides examples of common operators like addition, subtraction, equality checking, and shows how operators like assignment, logical AND, and bitwise XOR work. Usage examples are given for arithmetic operations, comparisons, and basic programs.
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
This document provides an introduction to Bayesian networks. It begins by explaining Bayesian networks using a medical example about determining the likelihood a patient has anthrax given various observed symptoms. It then provides a probability primer covering random variables, conditional probability, and independence. The document defines Bayesian networks as consisting of a directed acyclic graph and conditional probability tables at each node. It explains how Bayesian networks compactly represent joint probability distributions and allow for inference queries. The challenges of exact versus approximate inference in large networks are also noted.
Huffman coding is a lossless data compression algorithm that assigns variable-length binary codes to characters based on their frequencies, with more common characters getting shorter codes. It builds a Huffman tree from the character frequencies where the root node has the total frequency and interior nodes branch left or right. To encode a message, it traverses the tree assigning 0s and 1s to the path taken. This simulation shows building the Huffman tree for a sample message and assigns codes to each character, compressing the data from 160 bits to 45 bits. Huffman coding has time complexity of O(n log n) and is commonly used in file compression, multimedia, and communication applications, providing efficient compression at the cost of slower encoding and
[Question Paper] Web Design and Internet Based Applications (Old Syllabus) [A...Mumbai B.Sc.IT Study
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - V [Web Design and Internet Based Applications] (Old Syllabus). [Year - April / 2014] . . . Solution Set of this Paper is Coming soon . . .
This document contains a 7 question exam on Visual Basic-6 covering various topics like arrays, controls, loops, functions, data types, formatting, and more. It includes both theoretical and coding questions requiring knowledge of concepts like objects, events, properties, constants, file handling, flow control, and working with controls like listboxes, combo boxes, and dialog boxes. Students are instructed to attempt question 1 and 4 others, with questions having multiple parts worth between 6-8 marks each. Proper formatting and examples are expected in the answers.
The document discusses cathode ray tubes (CRTs), which were the primary output devices in graphical systems and video monitors. CRTs display images using an electron beam that scans across phosphorescent screen coatings. There are two types of display methods: raster scan and random scan. Color CRTs use three electron guns and phosphors to produce red, green, and blue light, and require convergence calibration. CRTs were used in televisions, computer monitors, radar displays, and oscilloscopes. They can produce bright, high quality images but also have disadvantages like health hazards from radiation emission and potential overheating issues.
The document discusses various software project size estimation metrics. It describes the limitations of lines of code (LOC) counting, such as variability due to coding style and not accounting for non-coding effort. Function point analysis and feature point analysis are presented as alternatives that overcome some LOC limitations by basing size on software features rather than lines. The key steps of function point analysis involve counting types of inputs, outputs, inquiries and other parameters to calculate unadjusted function points which are then adjusted based on technical complexity factors. While more accurate than LOC, function point analysis is still subjective based on how parameters are defined and counted.
The branch-and-bound method is used to solve optimization problems by traversing a state space tree. It computes a bound at each node to determine if the node is promising. Better approaches traverse nodes breadth-first and choose the most promising node using a bounding heuristic. The traveling salesperson problem is solved using branch-and-bound by finding an initial tour, defining a bounding heuristic as the actual cost plus minimum remaining cost, and expanding promising nodes in best-first order until finding the minimal tour.
closure properties of regular language.pptxThirumoorthy64
1) Regular languages have closure properties where certain operations on regular languages produce new regular languages. These include operations like union, concatenation, and Kleene star.
2) Finite automata have several decidable properties including emptiness, non-emptiness, finiteness, infiniteness, membership, and equality. These properties can be checked through operations on the automata like removing unreachable states.
3) Pushdown automata extend finite automata with a stack. They can be defined by their states, input symbols, stack symbols, transitions, and acceptance conditions like empty stack. Parse trees provide a hierarchical representation of how the symbols in a string derive from a grammar's starting symbol.
Sets are unordered collections of unique elements. Elements within a set cannot be accessed by index since sets are unordered. Common set operations include union, intersection, difference, and symmetric difference. Sets can be created using curly brackets or the set() function. Items can be added and removed from sets using methods like add(), remove(), discard(), and clear(). The length of a set can be determined using len(). Mathematical set relationships like subset, superset, and disjointness can be tested using methods like issubset(), issuperset(), and isdisjoint().
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
The document discusses component-based software engineering and defines a software component. A component is a modular building block defined by interfaces that can be independently deployed. Components are standardized, independent, composable, deployable, and documented. They communicate through interfaces and are designed to achieve reusability. The document outlines characteristics of components and discusses different views of components, including object-oriented, conventional, and process-related views. It also covers topics like component-level design principles, packaging, cohesion, and coupling.
This document discusses Python database programming. It introduces databases and how they store data in tables connected through columns. It discusses SQL for creating, accessing, and manipulating database data. It then discusses how Python supports various databases and database operations. It covers the Python DB-API for providing a standard interface for database programming. It provides examples of connecting to a database, executing queries, and retrieving and inserting data.
Time complexity analysis specifies how the running time of an algorithm depends on the size of its input. It is determined by identifying the basic operation of the algorithm and counting how many times it is executed as the input size increases. The time complexity of an algorithm can be expressed as a function T(n) relating the input size n to the running time. Common time complexities include constant O(1), linear O(n), logarithmic O(log n), and quadratic O(n^2). The order of growth indicates how fast the running time grows as n increases and is used to compare the efficiency of algorithms.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
This document provides an overview of computer graphics and its applications. It discusses various types of video display devices used in computer graphics like CRTs and flat panel displays. It describes how raster scan and random scan systems work and lists common input and output devices. The document outlines different chapters that will cover topics like line and curve generation algorithms, transformations, 3D viewing, surface detection, and modeling techniques. It provides examples of how computer graphics is used in fields like CAD, presentations, entertainment, education, visualization, image processing, and graphical user interfaces.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Tra...Edureka!
This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014. Below are the topics covered in this tutorial:
1. What is Data Analysis?
2. What is Pandas?
3. Pandas Operations
4. Use-case
This document discusses testing off-the-shelf (COTS) components. It defines COTS components as independently developed and reusable parts that are selected from a repository and assembled to build software systems. While COTS components reduce development costs and time, they present challenges to testing due to being treated as black boxes without access to requirements or development processes. The document outlines types of COTS component testing, including black-box testing of inputs/outputs, fault injection to evaluate error handling, operational testing in integrated systems, and interface propagation analysis to observe impacts of faults between components.
This document summarizes Python operators including arithmetic, assignment, comparison, logical, identity, membership, and bitwise operators. It provides examples of common operators like addition, subtraction, equality checking, and shows how operators like assignment, logical AND, and bitwise XOR work. Usage examples are given for arithmetic operations, comparisons, and basic programs.
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
This document provides an introduction to Bayesian networks. It begins by explaining Bayesian networks using a medical example about determining the likelihood a patient has anthrax given various observed symptoms. It then provides a probability primer covering random variables, conditional probability, and independence. The document defines Bayesian networks as consisting of a directed acyclic graph and conditional probability tables at each node. It explains how Bayesian networks compactly represent joint probability distributions and allow for inference queries. The challenges of exact versus approximate inference in large networks are also noted.
Huffman coding is a lossless data compression algorithm that assigns variable-length binary codes to characters based on their frequencies, with more common characters getting shorter codes. It builds a Huffman tree from the character frequencies where the root node has the total frequency and interior nodes branch left or right. To encode a message, it traverses the tree assigning 0s and 1s to the path taken. This simulation shows building the Huffman tree for a sample message and assigns codes to each character, compressing the data from 160 bits to 45 bits. Huffman coding has time complexity of O(n log n) and is commonly used in file compression, multimedia, and communication applications, providing efficient compression at the cost of slower encoding and
[Question Paper] Web Design and Internet Based Applications (Old Syllabus) [A...Mumbai B.Sc.IT Study
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - V [Web Design and Internet Based Applications] (Old Syllabus). [Year - April / 2014] . . . Solution Set of this Paper is Coming soon . . .
This document contains a 7 question exam on Visual Basic-6 covering various topics like arrays, controls, loops, functions, data types, formatting, and more. It includes both theoretical and coding questions requiring knowledge of concepts like objects, events, properties, constants, file handling, flow control, and working with controls like listboxes, combo boxes, and dialog boxes. Students are instructed to attempt question 1 and 4 others, with questions having multiple parts worth between 6-8 marks each. Proper formatting and examples are expected in the answers.
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - V [Advanced Java] (Old Syllabus). [Year - April / 2014] . . . Solution Set of this Paper is Coming soon . . .
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This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - V [ASP.NET With C#] (60:40 Pattern). [Year - October / 2013] . . . Solution Set of this Paper is Coming soon . . .
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This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - V [Advanced Java] (60:40 Pattern). [Year - April / 2014] . . . Solution Set of this Paper is Coming soon . . .
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This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - V [SQL – 2] (Old Syllabus). [Year - April / 2014] . . . Solution Set of this Paper is Coming soon . . .
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This document is a question paper for the ASP.NET with C# course for semester 5 of the B.Sc. IT program. It contains 7 sections with a total of 27 multiple choice and descriptive questions worth 100 marks. The questions cover topics like C# concepts, Windows forms, ASP.NET controls and features, ADO.NET, jQuery, web services and more. Students are instructed to answer 15 out of the 27 questions in detail within the allotted time of 3 hours.
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Internet Of Things (November – 2018) [Choice Based | Question Paper]Mumbai B.Sc.IT Study
This document is an exam paper for the subject Internet of Things from Mumbai University. It contains 15 questions divided into 5 sections. Section 1 contains 6 questions on defining and explaining key concepts related to IoT. Section 2 contains 6 questions on prototyping embedded devices and comparing development platforms like Arduino and Raspberry Pi. Section 3 contains 6 questions on different methods of prototyping like non-digital methods, 3D printing and laser cutting. Section 4 contains 6 questions on limitations of memory in embedded systems, performance concerns and libraries. Section 5 contains 6 questions on designing PCBs, certification of IoT devices, privacy concerns and the sensor commons project. Students have to attempt 3 questions from each section and the exam is
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The document provides information about a project management textbook for the BSc IT program at Mumbai University. It includes details like the university, course, semester, subject, and author. The author, Kamal Thakur, provides his contact information and outlines the contents of the textbook, which covers topics like software development planning, requirements, architecture, automation, metrics, and future trends in project management. It contains several chapters with questions and solutions related to project management concepts.
Project Management (April - 2015) [CBSGS - Paper Solution] {Mumbai University}Mumbai B.Sc.IT Study
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This document provides information about a project management paper solution from Mumbai University for the B.Sc. IT program. It includes the university details, course information, author information, and the paper itself which contains 7 sections with multiple choice questions covering various aspects of project management like the project life cycle, cost estimation, quality parameters, workflows, and more.
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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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In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
Assessment and Planning in Educational technology.pptx
[Question Paper] Web Design and Internet Based Applications (Old Syllabus) [April / 2013]
1. S Q L – 2
Q u e s t i o n P a p e r ( A p r i l – 2 0 1 3 ) [ O l d S y l l a b u s ]
1 | Page
M u m b a i B . S c . I T S t u d y
F a c e b o o k | T w i t t e r | I n s t a g r a m | G o o g l e + | Y o u T u b e
– Kamal T.
Time: 3 Hours Total Marks: 100
N.B.: (1) Question No. 1 is Compulsory.
(2) Attempt any four questions from Q.2 – Q.7.
(3) Make suitable assumption whenever necessary and state the assumption made.
(4) Answers to the same questions must be written together.
(5) Numbers to the right indicate marks.
(6) Draw a neat labelled diagram and give example whenever necessary.
Q.1 Attempt The Following Question: (20 Marks)
(A) Explain the accelerator keys in HTML. (5)
(B) What are Core HTML attributes? Explain ID and Class Attributes. (5)
(C) Write a VB Scripts program to print first 100 prime numbers. (5)
(D) Explain the buffer property of Response Object. (5)
Q.2 Attempt The Following Question: (20 Marks)
(A) Explain <table> tag. Explain the following tags <th>, <tfoot>, <tr>, <td>, <rowspan>
and <colspan> with examples.
(8)
(B) Explain the Response Object and Binary Write, End, Redirect and Write methods of
the Response Object.
(6)
(C) Differentiate between the GET and the POST methods. (6)
Q.3 Attempt The Following Question: (20 Marks)
(A) Explain Image Maps with example. (8)
(B) Explain server.transfer and server.execute methods with examples. (6)
(C) What is Active Server Pages? What is Internet Information Service? Explain the
working of Active Server Pages.
(6)
Q.4 Attempt The Following Question: (20 Marks)
(A) Explain the following tags with examples:
<i>, <b>, <u>, <sup>, <sub>, <pre>, <s>, <a href=”abc.html”>
(8)
(B) What is global.asa file? Explain its purpose with example. (6)
(C) Explain Expires and ExpiresAbsolute properties of the response object with examples (6)
2. S Q L – 2
Q u e s t i o n P a p e r ( A p r i l – 2 0 1 3 ) [ O l d S y l l a b u s ]
2 | Page
M u m b a i B . S c . I T S t u d y
F a c e b o o k | T w i t t e r | I n s t a g r a m | G o o g l e + | Y o u T u b e
– Kamal T.
Q.5 Attempt The Following Question: (20 Marks)
(A) Explain the ordered and unordered list tags with all attributes. Give example for each. (8)
(B) Explain the ADRotator Component with example. (6)
(C) What are Style Sheets? Explain the use of style sheets with examples. (6)
Q.6 Attempt The Following Question: (20 Marks)
(A) Explain the <FRAMESET> and <FRAME> tags with examples. (8)
(B) Write a VB Script to accept a number in a text box and display if it is even or odd in a
Message Box.
(6)
(C) What are the different ways to connect ways to connect to Database in ASP? Explain. (6)
Q.7 Attempt The Following Question: (20 Marks)
(A) Design student detail form which contains text box to accept the first name, last name,
Text Area to accept the Address of the student, Date of Birth three combo boxes one
for days (1 to 31), one for month (January to December), one for year (1985 to 2012),
Use radio button to accept the gender (male, female) and Stream (Science, Commerce,
Arts), Use Check Box to accept the hobbies (reading, outdoor games, watching TV,
Internet Surfing) and contains the two button (Submit and Reset Button).
(8)
(B) Write the code for validating the first name and last name text boxes to accept only
characters. Once the date of Birth is entered, display the age in message box.
(6)
(C) Write ASP code so that on click on the submit button, the data should be entered in
the database. Create suitable table. Display the details from the database on a new
web page.
(6)