#circular queue algorithm

[drmikewatts]

Soft Computing, Volume 29, Issue 6, March 2025

1) On subresiduated lattice ordered commutative monoids and some of its subvarieties

Author(s): Juan Manuel Cornejo, Hernán Javier San Martín, Valeria Anahí Sígal

Pages: 2517 - 2529

2) Dynamic updating approximations of local multigranulation neighborhood covering rough sets

Author(s): Qi Shi, Yan-Lan Zhang

Pages: 2531 - 2550

3) Relationships between various ideals in L-algebra

Author(s): S. Saidi Goraghani, R. A. Borzooei

Pages: 2551 - 2562

4) Numerical algorithm for solving third-order Emden–Fowler type pantograph differential equations: Taylor operational matrix method

Author(s): Nikita Saha, Randhir Singh

Pages: 2563 - 2579

5) L-fuzzy transforms: an operator-oriented approach

Author(s): A. P. Singh, S. P. Tiwari, I. Perfilieva

Pages: 2581 - 2593

6) A golden section-based group decision-making approach to software trustworthiness evaluation

Author(s): Chuan Yue

Pages: 2595 - 2618

7) The impact of hydrogen energy storage aqua electrolyzer fuels cell on automatic generation control of power system using optimal FGS PID controller

Author(s): Ankur Rai, Dushmanta Kumar Das

Pages: 2619 - 2638

8) Choquet integral in ranking data: on the construction of a measure of importance and interaction

Author(s): M. Eugenia Cornejo, Jesús Medina, Andreja Tepavčević

Pages: 2639 - 2651

9) Applications of integrals and extreme value of solution of uncertain age-dependent population equation with migration source

Author(s): Lu Yang

Pages: 2653 - 2668

10) A modified slacks-based measure of efficiency and super-efficiency under zero and negative data

Author(s): Ehsan Zanboori

Pages: 2669 - 2686

11) Optimal stress levels and inference procedures for constant stress accelerated life test under bathtub-shaped lifetime model

Author(s): Çağatay Çetinkaya

Pages: 2687 - 2714

12) Fc-gcn: A formal concept-enhanced graph convolution network model

Author(s): Kai Wu, Chao Zhang, Vincenzo Loia

Pages: 2715 - 2725

13) A model-free feature extraction procedure for interval-valued time series prediction

Author(s): Wan Tian, Zhongfeng Qin, Tao Hu

Pages: 2727 - 2741

14) Neutrosophic multi-choice 4D-transportation problem for different quality items

Author(s): Dipankar Chakraborty, Sarbari Samanta, Dipak Kumar Jana

Pages: 2743 - 2767

15) Optimizing energy harvesting in web of things services: a metaheuristic approach with retrial queue under working vacation

Author(s): N. Micheal Mathavavisakan, K. Indhira, Sherif I. Ammar

Pages: 2769 - 2790

16) Differentiation or undifferentiation? analysis of car-sharing strategies for the vehicle manufacturer

Author(s): Xin Yu, Zhongwei Chen, Zhi-Ping Fan

Pages: 2791 - 2806

17) A two-stage optimization replacement policy with multiple spare units

Author(s): Chunxiao Zhang, Hejiao Shen, Chao Yan

Pages: 2807 - 2817

18) A new model for facility bus terminal location problem based on modified Kerre’s inequality

Author(s): Sahar Rahdar, Reza Ghanbari, Donya Heidari

Pages: 2819 - 2828

19) A new metaheuristic optimization algorithm based on the participation of smart students to increase the class performance

Author(s): Einollah Pira, Alireza Rouhi

Pages: 2829 - 2844

20) A novel stochastic machine learning approach for resilient-leagile supplier selection: a circular supply chain in the era of industry 4.0

Author(s): Bahar Javan Molaei, Mohssen Ghanavati-Nejad, Mohammad Sheikhalishahi

Pages: 2845 - 2866

21) An arithmetic–geometric mean difference based approach to consistency index and priority vector of pairwise comparison matrices

Author(s): Yuan-Kai Hu, Fang Liu, Shi-Shan Wang

Pages: 2867 - 2884

22) On pure ideals in semilattices

Author(s): Nilesh Mundlik, Mayur Kshirsagar

Pages: 2885 - 2889

23) Innovative image encryption approach based on bitwise XOR high nonlinear S-boxes and random permutation

Author(s): Fırat Artuğer

Pages: 2891 - 2903

24) Evolutionary optimization in VANET services: a comprehensive survey, challenges and futuristic approach

Author(s): Madhuri Badole, Anuradha Thakare, Diego Oliva

Pages: 2905 - 2929

25) Wind farm layout optimization based on Harris hawk optimization algorithm with variable weight coefficients and Jansen wake model

Author(s): Min Wang, Jie-Sheng Wang, Yu-Cai Wang

Pages: 2931 - 2969

26) Construction and empirical research of a subjective-and-objective-analysis model of the online learning behavior

Author(s): Ke Wang, Kun Zheng, Ying Wang

Pages: 2971 - 2982

27) A synchronous spatiotemporal graph neural network for short-term traffic flow prediction

Author(s): Zhao Zhang, Xiaohong Jiao

Pages: 2983 - 2997

28) Modeling of metaheuristic-based dual cluster head selection with routing protocol for energy-efficient wireless sensor networks

Author(s): Maddikera Krishna Reddy, Anusha Sowbarnika Veluswamy, Syed Shameem

Pages: 2999 - 3020

29) Expansive detector via hybrid temporal and transposed convolutional mechanism for weld proximity defects

Author(s): Zihua Chen, Runmei Zhang, Kuan-ching Li

Pages: 3021 - 3034

30) Forecasting of exchange rate time series based on event-aware transformer mode

Author(s): Siyi Zhang, Tong Che, Ping Feng

Pages: 3035 - 3045

31) Sustainability, innovation and knowledge: a complex relationship based on an empirical study

Author(s): Sebastian-Ion Ceptureanu, Eduard-Gabriel Ceptureanu, Adriana Lavinia Iancu

Pages: 3047 - 3062

32) Why does economics need complexity?

Author(s): Mauro Gallegati, Silvia Gallegati

Pages: 3063 - 3072

33) A review for finding determinants of some band matrices

Author(s): Maryam Shams Solary

Pages: 3073 - 3082

34) Modeling and simulating a startup ecosystem development with a delayed differential equation

Author(s): Alessio Brini, Viviana Fanelli

Pages: 3083 - 3095

35) Explainable Grouped Deep Echo State Network for EEG-based emotion recognition

Author(s): Samar Bouazizi, Hela Ltifi

Pages: 3097 - 3114

[finarena]

DSA Channel: The Ultimate Destination for Learning Data Structures and Algorithms from Basics to Advanced

DSA mastery stands vital for successful software development and competitive programming in the current digital world that operates at high speeds. People at every skill level from beginner to advanced developer will find their educational destination at the DSA Channel.

Why is DSA Important?

Software development relies on data structures together with algorithms as its essential core components. Code optimization emerges from data structures and algorithms which produces better performance and leads to successful solutions of complex problems. Strategic knowledge of DSA serves essential needs for handling job interviews and coding competitions while enhancing logical thinking abilities. Proper guidance makes basic concepts of DSA both rewarding and enjoyable to study.

What Makes DSA Channel Unique?

The DSA Channel exists to simplify both data structures along algorithms and make them accessible to all users. Here’s why it stands out:

The channel provides step-by-step learning progress which conservatively begins by teaching arrays and linked lists and continues to dynamic programming and graph theory.

Each theoretical concept gets backed through coding examples practically to facilitate easier understanding and application in real-life situations.

Major companies like Google, Microsoft, and Amazon utilize DSA knowledge as part of their job recruiter process. Through their DSA Channel service candidates can perform mock interview preparation along with receiving technical interview problem-solving advice and interview cracking techniques.

Updates Occur Regularly Because the DSA Channel Matches the Ongoing Transformation in the Technology Industry. The content uses current algorithm field trends and new elements for constant updates.

 DSAC channels will be covering the below key topics

DSA Channel makes certain you have clear ideas that are necessary for everything from the basics of data structures to the most sophisticated methods and use cases. Highlights :

1. Introduction Basic Data Structures

Fundamentals First, You Always Need To Start With the Basics. Some of the DSA Channel topics are:

Memories storing and manipulating elements of Arrays

Linked Lists — learn linked lists: Singly Linked lists Dually linked lists and Circular linked list

Implementing Stacks and Queues — linear data structure with these implementations.

Hash Table: Understanding Hashing and its impact in the retrieval of Data.

2. Advanced Data Structures

If you want to get Intense: the DSA channel has profound lessons:

Graph bases Types- Type of Graph Traversals: BFS, DFS

Heaps — Come to know about Min Heap and Max Heap

Index Tries – How to store and retrieve a string faster than the fastest possible.

3. Algorithms

This is especially true for efficient problem-solving. The DSA Channel discusses in-depth:

Searching Algorithms Binary Search and Linear Search etc.

Dynamic Programming: Optimization of subproblems

Recursion and Backtracking: How to solve a problem by recursion.

Graph Algorithms — Dijkstra, Bellman-Ford and Floyd-Warshall etc

4. Applications of DSA in Real life

So one of the unique things (About the DSA channel) is these real-world applications of his DSA Channel.

Instead of just teaching Theory the channel gives a hands-on to see how it's used in world DSA applications.

Learning about Database Management Systems — Indexing, Query Optimization, Storage Techniques

Operating Systems – study algorithms scheduling, memory management,t, and file systems.

Machine Learning and AI — Learning the usage of algorithms in training models, and optimizing computations.

Finance and Banking — data structures that help us in identifying risk scheme things, fraud detection, transaction processing, etc.

This hands-on approach to working out will ensure that learners not only know how to use these concepts in real-life examples. 

How Arena Fincorp Benefits from DSA?

Arena Fincorp, a leading financial services provider, understands the importance of efficiency and optimization in the fintech sector. The financial solutions offered through Arena Fincorp operate under the same principles as data structures and algorithms which enhance coding operations. Arena Fincorp guarantees perfect financial transactions and data protection through its implementation of sophisticated algorithms. The foundational principles of DSA enable developers to build strong financial technological solutions for contemporary financial complications.

How to Get Started with DSA Channel?

New users of the DSA Channel should follow these instructions to maximize their experience:

The educational process should start with fundamental videos explaining arrays together with linked lists and stacks to establish a basic knowledge base.

The practice of DSA needs regular exercise and time to build comprehension. Devote specific time each day to find solutions for problems.

The platforms LeetCode, CodeChef, and HackerRank provide various DSA problems for daily problem-solving which boosts your skills.

Join community discussions where you can help learners by sharing solutions as well as working with fellow participants.

Students should do Mock Interviews through the DSA Channel to enhance their self-confidence and gain experience in actual interview situations.

The process of learning becomes more successful when people study together in a community. Through the DSA Channel students find an energetic learning community to share knowledge about doubts and project work and they exchange insight among themselves.

Conclusion

Using either data structures or algorithms in tech requires mastery so they have become mandatory in this sector. The DSA Channel delivers the best learning gateway that suits students as well as professionals and competitive programmers. Through their well-organized educational approach, practical experience and active learner network the DSA Channel builds a deep understanding of DSA with effective problem-solving abilities.

The value of data structures and algorithms and their optimized algorithms and efficient coding practices allows companies such as Arena Fincorp to succeed in their industries. New learners should begin their educational journey right now with the DSA Channel to master data structures and algorithms expertise.

[sunbeaminfo]

Enhance Your Coding Skills with Data Structures and Algorithms Classes at Sunbeam Institute, Pune

Elevate your programming expertise by enrolling in the Data Structures and Algorithms course at Sunbeam Institute, Pune. This comprehensive program is designed for students, freshers, and working professionals aiming to deepen their understanding of essential data structures and algorithms using Java.

Course Highlights:

Algorithm Analysis: Learn to evaluate time and space complexity for efficient coding.

Linked Lists: Master various types, including singly, doubly, and circular linked lists.

Stacks and Queues: Understand their implementation using arrays and linked lists, and apply them in expression evaluation and parenthesis balancing.

Sorting and Searching: Gain proficiency in algorithms like Quick Sort, Merge Sort, Heap Sort, Linear Search, Binary Search, and Hashing.

Trees and Graphs: Explore tree traversals, Binary Search Trees (BST), and graph algorithms such as Prim’s MST, Kruskal’s MST, Dijkstra's, and A* search.

Course Details:

Duration: 60 hours

Schedule: Weekdays (Monday to Saturday), 5:00 PM to 8:00 PM

Upcoming Batch: January 27, 2025, to February 18, 2025

Fees: ₹7,500 (including 18% GST)

Prerequisites:

Basic knowledge of Java programming, including classes, objects, generics, and Java collections (e.g., ArrayList).

Why Choose Sunbeam Institute?

Sunbeam Institute is renowned for its effective IT training programs in Pune, offering a blend of theoretical knowledge and practical application to ensure a thorough understanding of complex concepts.

Enroll Now: Secure your spot in this sought-after course to advance your programming skills and enhance your career prospects. For registration and more information, visit:

[fortunatelycoldengineer]

Linear Queue vs Circular Queue . . . Explore this essential Data structure! It covers key topics and resources for every tech enthusiast from algorithms to Data Structure. Perfect for learning and growth. Let's connect and innovate together! Check the link below: https://bit.ly/4iycWIJ

[nocodehackathon]

Essential Algorithms and Data Structures for Competitive Programming

Competitive programming is a thrilling and intellectually stimulating field that challenges participants to solve complex problems efficiently and effectively. At its core, competitive programming revolves around algorithms and data structures—tools that help you tackle problems with precision and speed. If you're preparing for a competitive programming contest or just want to enhance your problem-solving skills, understanding essential algorithms and data structures is crucial. In this blog, we’ll walk through some of the most important ones you should be familiar with.

1. Arrays and Strings

Arrays are fundamental data structures that store elements in a contiguous block of memory. They allow for efficient access to elements via indexing and are often the first data structure you encounter in competitive programming.

Operations: Basic operations include traversal, insertion, deletion, and searching. Understanding how to manipulate arrays efficiently can help solve a wide range of problems.

Strings are arrays of characters and are often used to solve problems involving text processing. Basic string operations like concatenation, substring search, and pattern matching are essential.

2. Linked Lists

A linked list is a data structure where elements (nodes) are stored in separate memory locations and linked together using pointers. There are several types of linked lists:

Singly Linked List: Each node points to the next node.

Doubly Linked List: Each node points to both the next and previous nodes.

Circular Linked List: The last node points back to the first node.

Linked lists are useful when you need to frequently insert or delete elements as they allow for efficient manipulation of the data.

3. Stacks and Queues

Stacks and queues are abstract data types that operate on a last-in-first-out (LIFO) and first-in-first-out (FIFO) principle, respectively.

Stacks: Useful for problems involving backtracking or nested structures (e.g., parsing expressions).

Queues: Useful for problems involving scheduling or buffering (e.g., breadth-first search).

Both can be implemented using arrays or linked lists and are foundational for many algorithms.

4. Hashing

Hashing involves using a hash function to convert keys into indices in a hash table. This allows for efficient data retrieval and insertion.

Hash Tables: Hash tables provide average-case constant time complexity for search, insert, and delete operations.

Collisions: Handling collisions (when two keys hash to the same index) using techniques like chaining or open addressing is crucial for effective hashing.

5. Trees

Trees are hierarchical data structures with a root node and child nodes. They are used to represent hierarchical relationships and are key to many algorithms.

Binary Trees: Each node has at most two children. They are used in various applications such as binary search trees (BSTs), where the left child is less than the parent, and the right child is greater.

Binary Search Trees (BSTs): Useful for dynamic sets where elements need to be ordered. Operations like insertion, deletion, and search have an average-case time complexity of O(log n).

Balanced Trees: Trees like AVL trees and Red-Black trees maintain balance to ensure O(log n) time complexity for operations.

6. Heaps

A heap is a specialized tree-based data structure that satisfies the heap property:

Max-Heap: The value of each node is greater than or equal to the values of its children.

Min-Heap: The value of each node is less than or equal to the values of its children.

Heaps are used in algorithms like heap sort and are also crucial for implementing priority queues.

7. Graphs

Graphs represent relationships between entities using nodes (vertices) and edges. They are essential for solving problems involving networks, paths, and connectivity.

Graph Traversal: Algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS) are used to explore nodes and edges in graphs.

Shortest Path: Algorithms such as Dijkstra’s and Floyd-Warshall help find the shortest path between nodes.

Minimum Spanning Tree: Algorithms like Kruskal’s and Prim’s are used to find the minimum spanning tree in a graph.

8. Dynamic Programming

Dynamic Programming (DP) is a method for solving problems by breaking them down into simpler subproblems and storing the results of these subproblems to avoid redundant computations.

Memoization: Storing results of subproblems to avoid recomputation.

Tabulation: Building a table of results iteratively, bottom-up.

DP is especially useful for optimization problems, such as finding the shortest path, longest common subsequence, or knapsack problem.

9. Greedy Algorithms

Greedy Algorithms make a series of choices, each of which looks best at the moment, with the hope that these local choices will lead to a global optimum.

Applications: Commonly used for problems like activity selection, Huffman coding, and coin change.

10. Graph Algorithms

Understanding graph algorithms is crucial for competitive programming:

Shortest Path Algorithms: Dijkstra’s Algorithm, Bellman-Ford Algorithm.

Minimum Spanning Tree Algorithms: Kruskal’s Algorithm, Prim’s Algorithm.

Network Flow Algorithms: Ford-Fulkerson Algorithm, Edmonds-Karp Algorithm.

Preparing for Competitive Programming: Summer Internship Program

If you're eager to dive deeper into these algorithms and data structures, participating in a summer internship program focused on Data Structures and Algorithms (DSA) can be incredibly beneficial. At our Summer Internship Program, we provide hands-on experience and mentorship to help you master these crucial skills. This program is designed for aspiring programmers who want to enhance their competitive programming abilities and prepare for real-world challenges.

What to Expect:

Hands-On Projects: Work on real-world problems and implement algorithms and data structures.

Mentorship: Receive guidance from experienced professionals in the field.

Workshops and Seminars: Participate in workshops that cover advanced topics and techniques.

Networking Opportunities: Connect with peers and industry experts to expand your professional network.

By participating in our DSA Internship, you’ll gain practical experience and insights that will significantly boost your competitive programming skills and prepare you for success in contests and future career opportunities.

In conclusion, mastering essential algorithms and data structures is key to excelling in competitive programming. By understanding and practicing these concepts, you can tackle complex problems with confidence and efficiency. Whether you’re just starting out or looking to sharpen your skills, focusing on these fundamentals will set you on the path to success.

Ready to take your skills to the next level? Join our Summer Internship Program and dive into the world of algorithms and data structures with expert guidance and hands-on experience. Your journey to becoming a competitive programming expert starts here!

[educationtech]

RPA Developer Salary, Roles & Responsibilities - Arya College

What Is Robotic Process Automation

Robotic process automation (RPA), also known as software robotics, uses automation technologies to mimic back-office tasks of human workers, such as extracting data, filling in forms, moving files, et cetera and It combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. This form of automation uses rule-based software to perform business process activities at a high volume, freeing up human resources to prioritize more complex tasks also RPA enables CIOs and other decision-makers to accelerate their digital transformation efforts and generate a higher return on investment (ROI) from their staff.

RPA Development

A software technology that makes it easy to build, deploy, and manage software robots that emulate human actions interacting with digital systems and software. Whether you are a graduate student, a working professional, or a business owner, this guide will provide you with valuable insights and upskilling advice to embark on a successful career in RPA development

RPA Development Process

Step 1: Obtain a degree in computer science or a related field. A strong foundation in computer science concepts is essential for understanding the principles behind RPA development. Step 2: Develop an algorithmic way of thinking and understanding through practice and continuous learning. Solve coding challenges, participate in programming competitions, and work on personal programming projects to enhance problem-solving abilities. Step 3: Gain knowledge and hands-on experience with RPA tools through training programs and online courses. Familiarise yourself with popular RPA platforms like UiPath, Automation Anywhere, and Blue These platforms offer comprehensive training programs that cover the fundamentals of RPA development. Step 4: Build a well-designed RPA Developer resume highlighting your relevant skills and experience. Include any internships or projects where you have worked on RPA development. Highlight your programming language proficiency and problem-solving abilities.

RPA training programs can be highly beneficial as they provide practical knowledge and industry recognition. These programs often include hands-on exercises and real-world case studies, allowing you to apply your skills practically. Additionally, they provide an avenue for networking with professionals in the field and staying updated with the latest trends and technologies

Robotic Process Automation Syllabus

Module: 1 – Robotic Automation Process Studio

Running a Process

Basic Skills

Process Validation

Decision Stage

Calculation Stage

Data Items

Module: 2 – Process Flow

Decisions

Circular Paths

Controlling Play

Set Next Stage

Breakpoints

Collections and Loops

Layers of Logic

Pages for Organization

Module: 3 – Inputs and outputs

Input Parameters

Stepping and Pages

Data Item Visibility

Data Types

Output Parameters

Start-up Parameters

Control Room

Process Outputs

Module: 4 – Business Objects

Object Studio

Business Objects

BLUE PRISM CONTENT

Action Stage

Inputs and Outputs

The Process Layer

Module: 5 – Object Studio

Creating a Business Object

Application Modeler

Spying Elements, Attributes

Attribute Selection, Launch, Wait, Timeouts, Terminate

Write, Press, Attach and Detach, Read, Actions

Action Inputs and Outputs, Data Items as Inputs

Module: 6 – Error Management

Exception Handling

Recover and Resume

Throwing Exceptions

Preserving the Current Exception

Exception Bubbling

Exception Blocks

Exception Handling in Practice

Module: 7 – Case Management

Queue Items Commercial in Confidence

BLUE PRISM CONTENT

Work Queue Configuration

Defer, Attempts, Pause and Resume

Filters Reports

Module: 8 – Additional Features

Safe Stop, Collection Actions

Choice Stage, Logging, Log Viewer, System Manager

Process/Business Object Grouping

Process and Object References, Export and Import

Module: 9 – Consolidation Exercise

Order System Process

Consolidation Exercise Checklist

Submitting Your Completed Solution

Read Full Blog : Arya College

[tutort-academy]

How to Ace Your DSA Interview, Even If You're a Newbie

Are you aiming to crack DSA interviews and land your dream job as a software engineer or developer? Look no further! This comprehensive guide will provide you with all the necessary tips and insights to ace your DSA interviews. We'll explore the important DSA topics to study, share valuable preparation tips, and even introduce you to Tutort Academy DSA courses to help you get started on your journey. So let's dive in!

Why is DSA Important?

Before we delve into the specifics of DSA interviews, let's first understand why data structures and algorithms are crucial for software development. DSA plays a vital role in optimizing software components, enabling efficient data storage and processing.

From logging into your Facebook account to finding the shortest route on Google Maps, DSA is at work in various applications we use every day. Mastering DSA allows you to solve complex problems, optimize code performance, and design efficient software systems.

Important DSA Topics to Study

To excel in DSA interviews, it's essential to have a strong foundation in key topics. Here are some important DSA topics you should study:

1. Arrays and Strings

Arrays and strings are fundamental data structures in programming. Understanding array manipulation, string operations, and common algorithms like sorting and searching is crucial for solving coding problems.

2. Linked Lists

Linked lists are linear data structures that consist of nodes linked together. It's important to understand concepts like singly linked lists, doubly linked lists, and circular linked lists, as well as operations like insertion, deletion, and traversal.

3. Stacks and Queues

Stacks and queues are abstract data types that follow specific orderings. Mastering concepts like LIFO (Last In, First Out) for stacks and FIFO (First In, First Out) for queues is essential. Additionally, learn about their applications in real-life scenarios.

4. Trees and Binary Trees

Trees are hierarchical data structures with nodes connected by edges. Understanding binary trees, binary search trees, and traversal algorithms like preorder, inorder, and postorder is crucial. Additionally, explore advanced concepts like AVL trees and red-black trees.

5. Graphs

Graphs are non-linear data structures consisting of nodes (vertices) and edges. Familiarize yourself with graph representations, traversal algorithms like BFS (Breadth-First Search) and DFS (Depth-First Search), and graph algorithms such as Dijkstra's algorithm and Kruskal's algorithm.

6. Sorting and Searching Algorithms

Understanding various sorting algorithms like bubble sort, selection sort, insertion sort, merge sort, and quicksort is essential. Additionally, familiarize yourself with searching algorithms like linear search, binary search, and hash-based searching.

7. Dynamic Programming

Dynamic programming involves breaking down a complex problem into smaller overlapping subproblems and solving them individually. Mastering this technique allows you to solve optimization problems efficiently.

These are just a few of the important DSA topics to study. It's crucial to have a solid understanding of these concepts and their applications to perform well in DSA interviews.

Tips to Follow While Preparing for DSA Interviews

Preparing for DSA interviews can be challenging, but with the right approach, you can maximize your chances of success. Here are some tips to keep in mind:

1. Understand the Fundamentals

Before diving into complex algorithms, ensure you have a strong grasp of the fundamentals. Familiarize yourself with basic data structures, common algorithms, and time and space complexities.

2. Practice Regularly

Consistent practice is key to mastering DSA. Solve a wide range of coding problems, participate in coding challenges, and implement algorithms from scratch. Leverage online coding platforms like LeetCode, HackerRank to practice and improve your problem-solving skills.

3. Analyze and Optimize

After solving a problem, analyze your solution and look for areas of improvement. Optimize your code for better time and space complexities. This demonstrates your ability to write efficient and scalable code.

4. Collaborate and Learn from Others

Engage with the coding community, join study groups, and participate in online forums. Collaborating with others allows you to learn different approaches, gain insights, and improve your problem-solving skills.

5. Mock Interviews and Feedback

Simulate real interview scenarios by participating in mock interviews. Seek feedback from experienced professionals or mentors who can provide valuable insights into your strengths and areas for improvement.

Following these tips will help you build a solid foundation in DSA and boost your confidence for interviews.

Conclusion

Mastering DSA is crucial for acing coding interviews and securing your dream job as a software engineer or developer. By studying important DSA topics, following effective preparation tips, and leveraging Tutort Academy's DSA courses, you'll be well-equipped to tackle DSA interviews with confidence. Remember to practice regularly, seek feedback, and stay curious.

Good luck on your DSA journey!

[deltainfoteklive]

Different Types of Non Preemptive CPU Scheduling Algorithms

In the world of operating systems, CPU scheduling algorithms play a crucial role in managing and optimizing the allocation of the CPU's processing power. When it comes to non-preemptive scheduling algorithms, there are various types that serve different purposes in ensuring efficient task execution and resource allocation. In this article, we will explore these different types of non-preemptive CPU scheduling algorithms and their practical applications. Introduction to Non-Preemptive CPU Scheduling Algorithms Non-preemptive CPU scheduling algorithms, as the name suggests, do not allow a running task to be interrupted by another task. The CPU remains allocated to a process until it voluntarily releases it or completes its execution. This approach provides simplicity and predictability but may lead to suboptimal resource utilization in dynamic environments. First Come, First Serve (FCFS) Scheduling Algorithm The First Come, First Serve (FCFS) scheduling algorithm is the simplest form of non-preemptive scheduling. It operates by allocating the CPU to the first process that arrives and stays in the ready queue until its completion. However, FCFS can suffer from the "convoy effect" when a long process holds up the execution of subsequent shorter processes. Shortest Job Next (SJN) Scheduling Algorithm The Shortest Job Next (SJN) scheduling algorithm aims to minimize the average waiting time by prioritizing processes based on their burst time. The process with the shortest burst time gets the CPU first, ensuring faster execution for shorter tasks. This algorithm is ideal for scenarios where the length of the tasks is known in advance. Priority Scheduling Algorithm In priority scheduling, each process is assigned a priority value, and the CPU is allocated to the highest priority process. This algorithm allows for effective resource allocation based on the importance or urgency of the tasks. However, it may lead to starvation if lower priority processes are consistently deprived of CPU time. Earliest Deadline First (EDF) Scheduling Algorithm The Earliest Deadline First (EDF) scheduling algorithm assigns deadlines to each process and gives priority to the process with the earliest deadline. It ensures timely execution of tasks with strict timing requirements, making it suitable for real-time systems. However, it requires accurate estimation and prediction of deadlines. Round Robin Scheduling Algorithm The Round Robin scheduling algorithm provides equal opportunity for each process by allocating a fixed time slice (quantum) to each process in a circular manner. This approach prevents starvation and provides fair CPU time sharing among processes. However, it may result in increased waiting time for longer processes. Multilevel Queue Scheduling Algorithm The Multilevel Queue scheduling algorithm categorizes processes into different priority queues, each with its own scheduling algorithm. This approach enables prioritization based on different criteria, such as process type, system level tasks, or user-level tasks. It allows for effective resource utilization based on the nature of the processes. Multilevel Feedback Queue Scheduling Algorithm The Multilevel Feedback Queue scheduling algorithm improves upon the Multilevel Queue algorithm by allowing processes to move between different queues based on their behavior. This adaptive approach enables dynamic adjustment of priorities based on the processes' execution history, allowing for improved resource allocation and fairness. Lottery Scheduling Algorithm The Lottery scheduling algorithm assigns tickets to each process, and a random lottery determines the winner of the CPU time. This approach provides probabilistic fairness, where processes with more tickets have higher chances of winning the CPU. It allows for a flexible distribution of CPU time based on resource requirements or priorities. Fair Share Scheduling Algorithm The Fair Share scheduling algorithm focuses on providing equal opportunity for all users or groups in a multi-user environment. It ensures that each user or group receives a fair share of the CPU time based on their relative resource allocation. This approach promotes equitable resource utilization in shared computing environments. Guaranteed Scheduling Algorithm The Guaranteed scheduling algorithm ensures that each process is guaranteed a minimum amount of CPU time, regardless of competing processes. This approach is beneficial for tasks with specific timing requirements or critical operations. It helps ensure the timely execution of critical processes and minimal disruption in real-time systems. Comparison of Non-Preemptive Scheduling Algorithms When comparing non-preemptive scheduling algorithms, several factors come into play, including CPU utilization, throughput, waiting time, response time, fairness, and real-time performance. Each algorithm has its own strengths and weaknesses, making them suitable for different scenarios based on the specific requirements and constraints. Challenges and Limitations of Non-Preemptive Scheduling Algorithms While non-preemptive scheduling algorithms offer simplicity and predictability, they also have limitations. One limitation is their inability to handle scenarios where tasks with shorter arrival times require immediate execution, leading to potential delays or missed deadlines. Additionally, these algorithms may not adapt well to dynamic workloads or varying priorities. Real-world Applications of Non-Preemptive Scheduling Algorithms Non-preemptive scheduling algorithms find applications in various domains, including real-time systems, industrial process control, multimedia processing, and embedded systems. These algorithms are prevalent in situations where predictable execution, guaranteed resource allocations, and optimized task sequencing are crucial. Conclusion In this article, we explored the different types of non-preemptive CPU scheduling algorithms and their applications. Each algorithm has its own characteristics and suitability based on specific requirements. Understanding these algorithms helps in making informed decisions when it comes to resource allocation and optimizing task execution in the world of operating systems. FAQs 1. What are the advantages of non-preemptive scheduling algorithms? Non-preemptive scheduling algorithms provide simplicity, predictability, and determinism. They ensure that once a process starts executing, it will not be interrupted, leading to fewer context switches and lower system overhead. 2. How do non-preemptive scheduling algorithms handle task priority? Non-preemptive scheduling algorithms prioritize tasks based on their arrival time or assigned priority values. The CPU is allocated to the task with the highest priority or the task that has been waiting the longest in the ready queue. 3. Are non-preemptive scheduling algorithms suitable for real-time systems? Yes, some non-preemptive scheduling algorithms, such as Earliest Deadline First (EDF), are specifically designed for real-time systems. They ensure the timely execution of tasks with strict timing requirements. 4. Can non-preemptive scheduling algorithms adapt to dynamic workloads? Non-preemptive scheduling algorithms are generally not as adaptable to dynamic workloads compared to preemptive scheduling algorithms. They may struggle to handle scenarios where tasks with shorter arrival times require immediate execution. 5. What are the challenges of using non-preemptive scheduling algorithms? One challenge of non-preemptive scheduling algorithms is the potential for delays or missed deadlines when tasks with shorter arrival times are present. These algorithms may also struggle to prioritize tasks effectively in highly dynamic environments. Read the full article

[allcprogrammingandalgorithm]

Write a C program to read the distance between two cities in KM. and print that distance in meters, feet, inches and centimeters.

Write a C program to read the distance between two cities in KM. and print that distance in meters, feet, inches and centimeters.

#include<stdio.h> void main() { float km, me, feet,inc,cm; printf("\n\n\tEnter is value::"); scanf("%f",&km); me=km*1000; cm=me*100; feet=cm*3780.5; inc=feet*12; printf("\n meters ::%f",me); printf("\n centimeter ::%f",cm); printf("\n feet ::%f",feet); printf("\n inches ::%f",inc); }

[courseforfree]

Data Structures and Algorithms from Zero to Hero and Crack Top Companies 100+ Interview questions (Java Coding)

What you’ll learn

Java Data Structures and Algorithms Masterclass

Learn, implement, and use different Data Structures

Learn, implement and use different Algorithms

Become a better developer by mastering computer science fundamentals

Learn everything you need to ace difficult coding interviews

Cracking the Coding Interview with 100+ questions with explanations

Time and Space Complexity of Data Structures and Algorithms

Recursion

Big O

Dynamic Programming

Divide and Conquer Algorithms

Graph Algorithms

Greedy Algorithms

Requirements

Basic Java Programming skills

Description

Welcome to the Java Data Structures and Algorithms Masterclass, the most modern, and the most complete Data Structures and Algorithms in Java course on the internet.

At 44+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Java. You will see 100+ Interview Questions done at the top technology companies such as Apple, Amazon, Google, and Microsoft and how-to face Interviews with comprehensive visual explanatory video materials which will bring you closer to landing the tech job of your dreams!

Learning Java is one of the fastest ways to improve your career prospects as it is one of the most in-demand tech skills! This course will help you in better understanding every detail of Data Structures and how algorithms are implemented in high-level programming languages.

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to succeed as a professional programmer.

After finishing this course, you will be able to:

Learn basic algorithmic techniques such as greedy algorithms, binary search, sorting, and dynamic programming to solve programming challenges.

Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications

Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets

Learn how to apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.

Why this course is so special and different from any other resource available online?

This course will take you from the very beginning to very complex and advanced topics in understanding Data Structures and Algorithms!

You will get video lectures explaining concepts clearly with comprehensive visual explanations throughout the course.

You will also see Interview Questions done at the top technology companies such as Apple, Amazon, Google, and Microsoft.

I cover everything you need to know about the technical interview process!

So whether you are interested in learning the top programming language in the world in-depth and interested in learning the fundamental Algorithms, Data Structures, and performance analysis that make up the core foundational skillset of every accomplished programmer/designer or software architect and is excited to ace your next technical interview this is the course for you!

And this is what you get by signing up today:

Lifetime access to 44+ hours of HD quality videos. No monthly subscription. Learn at your own pace, whenever you want

Friendly and fast support in the course Q&A whenever you have questions or get stuck

FULL money-back guarantee for 30 days!

This course is designed to help you to achieve your career goals. Whether you are looking to get more into Data Structures and Algorithms, increase your earning potential, or just want a job with more freedom, this is the right course for you!

The topics that are covered in this course.

Section 1 – Introduction

What are Data Structures?

What is an algorithm?

Why are Data Structures And Algorithms important?

Types of Data Structures

Types of Algorithms

Section 2 – Recursion

What is Recursion?

Why do we need recursion?

How does Recursion work?

Recursive vs Iterative Solutions

When to use/avoid Recursion?

How to write Recursion in 3 steps?

How to find Fibonacci numbers using Recursion?

Section 3 – Cracking Recursion Interview Questions

Question 1 – Sum of Digits

Question 2 – Power

Question 3 – Greatest Common Divisor

Question 4 – Decimal To Binary

Section 4 – Bonus CHALLENGING Recursion Problems (Exercises)

power

factorial

products array

recursiveRange

fib

reverse

palindrome

some recursive

flatten

capitalize first

nestedEvenSum

capitalize words

stringifyNumbers

collects things

Section 5 – Big O Notation

Analogy and Time Complexity

Big O, Big Theta, and Big Omega

Time complexity examples

Space Complexity

Drop the Constants and the nondominant terms

Add vs Multiply

How to measure the codes using Big O?

How to find time complexity for Recursive calls?

How to measure Recursive Algorithms that make multiple calls?

Section 6 – Top 10 Big O Interview Questions (Amazon, Facebook, Apple, and Microsoft)

Product and Sum

Print Pairs

Print Unordered Pairs

Print Unordered Pairs 2 Arrays

Print Unordered Pairs 2 Arrays 100000 Units

Reverse

O(N)  Equivalents

Factorial Complexity

Fibonacci Complexity

Powers of 2

Section 7 – Arrays

What is an Array?

Types of Array

Arrays in Memory

Create an Array

Insertion Operation

Traversal Operation

Accessing an element of Array

Searching for an element in Array

Deleting an element from Array

Time and Space complexity of One Dimensional Array

One Dimensional Array Practice

Create Two Dimensional Array

Insertion – Two Dimensional Array

Accessing an element of Two Dimensional Array

Traversal – Two Dimensional Array

Searching for an element in Two Dimensional Array

Deletion – Two Dimensional Array

Time and Space complexity of Two Dimensional Array

When to use/avoid array

Section 8 – Cracking Array Interview Questions (Amazon, Facebook, Apple, and Microsoft)

Question 1 – Missing Number

Question 2 – Pairs

Question 3 – Finding a number in an Array

Question 4 – Max product of two int

Question 5 – Is Unique

Question 6 – Permutation

Question 7 – Rotate Matrix

Section 9 – CHALLENGING Array Problems (Exercises)

Middle Function

2D Lists

Best Score

Missing Number

Duplicate Number

Pairs

Section 10 – Linked List

What is a Linked List?

Linked List vs Arrays

Types of Linked List

Linked List in the Memory

Creation of Singly Linked List

Insertion in Singly Linked List in Memory

Insertion in Singly Linked List Algorithm

Insertion Method in Singly Linked List

Traversal of Singly Linked List

Search for a value in Single Linked List

Deletion of a node from Singly Linked List

Deletion Method in Singly Linked List

Deletion of entire Singly Linked List

Time and Space Complexity of Singly Linked List

Section 11 – Circular Singly Linked List

Creation of Circular Singly Linked List

Insertion in Circular Singly Linked List

Insertion Algorithm in Circular Singly Linked List

Insertion method in Circular Singly Linked List

Traversal of Circular Singly Linked List

Searching a node in Circular Singly Linked List

Deletion of a node from Circular Singly Linked List

Deletion Algorithm in Circular Singly Linked List

A method in Circular Singly Linked List

Deletion of entire Circular Singly Linked List

Time and Space Complexity of Circular Singly Linked List

Section 12 – Doubly Linked List

Creation of Doubly Linked List

Insertion in Doubly Linked List

Insertion Algorithm in Doubly Linked List

Insertion Method in Doubly Linked List

Traversal of Doubly Linked List

Reverse Traversal of Doubly Linked List

Searching for a node in Doubly Linked List

Deletion of a node in Doubly Linked List

Deletion Algorithm in Doubly Linked List

Deletion Method in Doubly Linked List

Deletion of entire Doubly Linked List

Time and Space Complexity of Doubly Linked List

Section 13 – Circular Doubly Linked List

Creation of Circular Doubly Linked List

Insertion in Circular Doubly Linked List

Insertion Algorithm in Circular Doubly Linked List

Insertion Method in Circular Doubly Linked List

Traversal of Circular Doubly Linked List

Reverse Traversal of Circular Doubly Linked List

Search for a node in Circular Doubly Linked List

Delete a node from Circular Doubly Linked List

Deletion Algorithm in Circular Doubly Linked List

Deletion Method in Circular Doubly Linked List

Entire Circular Doubly Linked List

Time and Space Complexity of Circular Doubly Linked List

Time Complexity of Linked List vs Arrays

Section 14 – Cracking Linked List Interview Questions (Amazon, Facebook, Apple, and Microsoft)

Linked List Class

Question 1 – Remove Dups

Question 2 – Return Kth to Last

Question 3 – Partition

Question 4 – Sum Linked Lists

Question 5 – Intersection

Section 15 – Stack

What is a Stack?

What and Why of Stack?

Stack Operations

Stack using Array vs Linked List

Stack Operations using Array (Create, isEmpty, isFull)

Stack Operations using Array (Push, Pop, Peek, Delete)

Time and Space Complexity of Stack using Array

Stack Operations using Linked List

Stack methods – Push, Pop, Peek, Delete, and isEmpty using Linked List

Time and Space Complexity of Stack using Linked List

When to Use/Avoid Stack

Stack Quiz

Section 16 – Queue

What is a Queue?

Linear Queue Operations using Array

Create, isFull, isEmpty, and enQueue methods using Linear Queue Array

Dequeue, Peek and Delete Methods using Linear Queue Array

Time and Space Complexity of Linear Queue using Array

Why Circular Queue?

Circular Queue Operations using Array

Create, Enqueue, isFull and isEmpty Methods in Circular Queue using Array

Dequeue, Peek and Delete Methods in Circular Queue using Array

Time and Space Complexity of Circular Queue using Array

Queue Operations using Linked List

Create, Enqueue and isEmpty Methods in Queue using Linked List

Dequeue, Peek and Delete Methods in Queue using Linked List

Time and Space Complexity of Queue using Linked List

Array vs Linked List Implementation

When to Use/Avoid Queue?

Section 17 – Cracking Stack and Queue Interview Questions (Amazon, Facebook, Apple, Microsoft)

Question 1 – Three in One

Question 2 – Stack Minimum

Question 3 – Stack of Plates

Question 4 – Queue via Stacks

Question 5 – Animal Shelter

Section 18 – Tree / Binary Tree

What is a Tree?

Why Tree?

Tree Terminology

How to create a basic tree in Java?

Binary Tree

Types of Binary Tree

Binary Tree Representation

Create Binary Tree (Linked List)

PreOrder Traversal Binary Tree (Linked List)

InOrder Traversal Binary Tree (Linked List)

PostOrder Traversal Binary Tree (Linked List)

LevelOrder Traversal Binary Tree (Linked List)

Searching for a node in Binary Tree (Linked List)

Inserting a node in Binary Tree (Linked List)

Delete a node from Binary Tree (Linked List)

Delete entire Binary Tree (Linked List)

Create Binary Tree (Array)

Insert a value Binary Tree (Array)

Search for a node in Binary Tree (Array)

PreOrder Traversal Binary Tree (Array)

InOrder Traversal Binary Tree (Array)

PostOrder Traversal Binary Tree (Array)

Level Order Traversal Binary Tree (Array)

Delete a node from Binary Tree (Array)

Entire Binary Tree (Array)

Linked List vs Python List Binary Tree

Section 19 – Binary Search Tree

What is a Binary Search Tree? Why do we need it?

Create a Binary Search Tree

Insert a node to BST

Traverse BST

Search in BST

Delete a node from BST

Delete entire BST

Time and Space complexity of BST

Section 20 – AVL Tree

What is an AVL Tree?

Why AVL Tree?

Common Operations on AVL Trees

Insert a node in AVL (Left Left Condition)

Insert a node in AVL (Left-Right Condition)

Insert a node in AVL (Right Right Condition)

Insert a node in AVL (Right Left Condition)

Insert a node in AVL (all together)

Insert a node in AVL (method)

Delete a node from AVL (LL, LR, RR, RL)

Delete a node from AVL (all together)

Delete a node from AVL (method)

Delete entire AVL

Time and Space complexity of AVL Tree

Section 21 – Binary Heap

What is Binary Heap? Why do we need it?

Common operations (Creation, Peek, sizeofheap) on Binary Heap

Insert a node in Binary Heap

Extract a node from Binary Heap

Delete entire Binary Heap

Time and space complexity of Binary Heap

Section 22 – Trie

What is a Trie? Why do we need it?

Common Operations on Trie (Creation)

Insert a string in Trie

Search for a string in Trie

Delete a string from Trie

Practical use of Trie

Section 23 – Hashing

What is Hashing? Why do we need it?

Hashing Terminology

Hash Functions

Types of Collision Resolution Techniques

Hash Table is Full

Pros and Cons of Resolution Techniques

Practical Use of Hashing

Hashing vs Other Data structures

Section 24 – Sort Algorithms

What is Sorting?

Types of Sorting

Sorting Terminologies

Bubble Sort

Selection Sort

Insertion Sort

Bucket Sort

Merge Sort

Quick Sort

Heap Sort

Comparison of Sorting Algorithms

Section 25 – Searching Algorithms

Introduction to Searching Algorithms

Linear Search

Linear Search in Python

Binary Search

Binary Search in Python

Time Complexity of Binary Search

Section 26 – Graph Algorithms

What is a Graph? Why Graph?

Graph Terminology

Types of Graph

Graph Representation

The graph in Java using Adjacency Matrix

The graph in Java using Adjacency List

Section 27 – Graph Traversal

Breadth-First Search Algorithm (BFS)

Breadth-First Search Algorithm (BFS) in Java – Adjacency Matrix

Breadth-First Search Algorithm (BFS) in Java – Adjacency List

Time Complexity of Breadth-First Search (BFS) Algorithm

Depth First Search (DFS) Algorithm

Depth First Search (DFS) Algorithm in Java – Adjacency List

Depth First Search (DFS) Algorithm in Java – Adjacency Matrix

Time Complexity of Depth First Search (DFS) Algorithm

BFS Traversal vs DFS Traversal

Section 28 – Topological Sort

What is Topological Sort?

Topological Sort Algorithm

Topological Sort using Adjacency List

Topological Sort using Adjacency Matrix

Time and Space Complexity of Topological Sort

Section 29 – Single Source Shortest Path Problem

what is Single Source Shortest Path Problem?

Breadth-First Search (BFS) for Single Source Shortest Path Problem (SSSPP)

BFS for SSSPP in Java using Adjacency List

BFS for SSSPP in Java using Adjacency Matrix

Time and Space Complexity of BFS for SSSPP

Why does BFS not work with Weighted Graph?

Why does DFS not work for SSSP?

Section 30 – Dijkstra’s Algorithm

Dijkstra’s Algorithm for SSSPP

Dijkstra’s Algorithm in Java – 1

Dijkstra’s Algorithm in Java – 2

Dijkstra’s Algorithm with Negative Cycle

Section 31 – Bellman-Ford Algorithm

Bellman-Ford Algorithm

Bellman-Ford Algorithm with negative cycle

Why does Bellman-Ford run V-1 times?

Bellman-Ford in Python

BFS vs Dijkstra vs Bellman Ford

Section 32 – All Pairs Shortest Path Problem

All pairs shortest path problem

Dry run for All pair shortest path

Section 33 – Floyd Warshall

Floyd Warshall Algorithm

Why Floyd Warshall?

Floyd Warshall with negative cycle,

Floyd Warshall in Java,

BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,

Section 34 – Minimum Spanning Tree

Minimum Spanning Tree,

Disjoint Set,

Disjoint Set in Java,

Section 35 – Kruskal’s and Prim’s Algorithms

Kruskal Algorithm,

Kruskal Algorithm in Python,

Prim’s Algorithm,

Prim’s Algorithm in Python,

Prim’s vs Kruskal

Section 36 – Cracking Graph and Tree Interview Questions (Amazon, Facebook, Apple, Microsoft)

Section 37 – Greedy Algorithms

What is a Greedy Algorithm?

Well known Greedy Algorithms

Activity Selection Problem

Activity Selection Problem in Python

Coin Change Problem

Coin Change Problem in Python

Fractional Knapsack Problem

Fractional Knapsack Problem in Python

Section 38 – Divide and Conquer Algorithms

What is a Divide and Conquer Algorithm?

Common Divide and Conquer algorithms

How to solve the Fibonacci series using the Divide and Conquer approach?

Number Factor

Number Factor in Java

House Robber

House Robber Problem in Java

Convert one string to another

Convert One String to another in Java

Zero One Knapsack problem

Zero One Knapsack problem in Java

Longest Common Sequence Problem

Longest Common Subsequence in Java

Longest Palindromic Subsequence Problem

Longest Palindromic Subsequence in Java

Minimum cost to reach the Last cell problem

Minimum Cost to reach the Last Cell in 2D array using Java

Number of Ways to reach the Last Cell with given Cost

Number of Ways to reach the Last Cell with given Cost in Java

Section 39 – Dynamic Programming

What is Dynamic Programming? (Overlapping property)

Where does the name of DC come from?

Top-Down with Memoization

Bottom-Up with Tabulation

Top-Down vs Bottom Up

Is Merge Sort Dynamic Programming?

Number Factor Problem using Dynamic Programming

Number Factor: Top-Down and Bottom-Up

House Robber Problem using Dynamic Programming

House Robber: Top-Down and Bottom-Up

Convert one string to another using Dynamic Programming

Convert String using Bottom Up

Zero One Knapsack using Dynamic Programming

Zero One Knapsack – Top Down

Zero One Knapsack – Bottom Up

Section 40 – CHALLENGING Dynamic Programming Problems

Longest repeated Subsequence Length problem

Longest Common Subsequence Length problem

Longest Common Subsequence  problem

Diff Utility

Shortest Common Subsequence  problem

Length of Longest Palindromic Subsequence

Subset Sum Problem

Egg Dropping Puzzle

Maximum Length Chain of Pairs

Section 41 – A Recipe for Problem Solving

Introduction

Step 1 – Understand the problem

Step 2 – Examples

Step 3 – Break it Down

Step 4 – Solve or Simplify

Step 5 – Look Back and Refactor

Section 41 – Wild West

Download

To download more paid courses for free visit course catalog where  1000+ paid courses available for free. You can get the full course into your device with just a single click. Follow the link above to download this course for free. 

[muffin-1-world]

Algorithms

Hi there!, Find here algorithms of implementing simple queue, using linked list and circular queue//

https://timecomplexity1.blogspot.com/2021/05/simple-queue.html

https://timecomplexity1.blogspot.com/2021/05/queue-using-linked-list.html

https://timecomplexity1.blogspot.com/2021/05/simple-queue-2.html

https://timecomplexity1.blogspot.com/2021/05/circular-queue-implementation.html

[bmharwani]

This video tutorial is on how to create a circular queue using array. This tutorial on data structures is for beginners and will make you understand how to implement a circular queue, how to create a circular queue in c using array and circular queue program in c using array. You will understand the complete program and will learn about, circular queue tutorialspoint, circular queue in data structure pdf along with circular queue example. By the end of video you will be knowing, circular queue in C using Array, circular queue in data structures and C program to implement circular queue operations. The tutorial explains with figures at each step and will make you understand the program to implement circular queue using arrays in C, circular queue implementation in C using array and circular queue implementation using array in C along with circular queue algorithm and insertion and deletion in circular queue in data structure too. You can download the program from the following link: http://bmharwani.com/circularqueuearr.c To see the video on linear queue, visit: https://www.youtube.com/watch?v=_OD_BHiDTWk&t=10s To understand pass by value and pass by reference, visit: https://www.youtube.com/watch?v=NIV7M4MSLs4&t=18s To see the video on circular linked list, visit: https://www.youtube.com/watch?v=lg-n_NHAeZk&t=1s For more videos on Data Structures, visit: https://www.youtube.com/watch?v=lg-n_NHAeZk&list=PLuDr_vb2LpAxVWIk-po5nL5Ct2pHpndLR To get notification for latest videos uploaded, subscribe to my channel: https://youtube.com/c/bintuharwani To see more videos on different computer subjects, visit: http://bmharwani.com

[sunbeaminfo]

Elevate your programming expertise by enrolling in the Data Structures and Algorithms course at Sunbeam Institute, Pune. This comprehensive program is designed for students, freshers, and working professionals aiming to deepen their understanding of essential data structures and algorithms using Java.

Course Highlights:

Algorithm Analysis: Learn to evaluate time and space complexity for efficient coding.

Linked Lists: Master various types, including singly, doubly, and circular linked lists.

Stacks and Queues: Understand their implementation using arrays and linked lists, and apply them in expression evaluation and parenthesis balancing.

Sorting and Searching: Gain proficiency in algorithms like Quick Sort, Merge Sort, Heap Sort, Linear Search, Binary Search, and Hashing.

Trees and Graphs: Explore tree traversals, Binary Search Trees (BST), and graph algorithms such as Prim’s MST, Kruskal’s MST, Dijkstra's, and A* search.

Course Details:

Duration: 60 hours

Schedule: Weekdays (Monday to Saturday), 5:00 PM to 8:00 PM

Upcoming Batch: January 27, 2025, to February 18, 2025

Fees: ₹7,500 (including 18% GST)

Prerequisites:

Basic knowledge of Java programming, including classes, objects, generics, and Java collections (e.g., ArrayList).

Why Choose Sunbeam Institute?

Sunbeam Institute is renowned for its effective IT training programs in Pune, offering a blend of theoretical knowledge and practical application to ensure a thorough understanding of complex concepts.

[fortunatelycoldengineer]

Circular Queue . . Explore this essential concept of Data Structures! It covers key topics and resources for every tech enthusiast from algorithms to Data Structure. Perfect for learning and growth. Let's connect and innovate together! . . Check the link below: https://bit.ly/3YKTMrV

[fufupaw]

Use Python language for this Assignment. Find the Resources (Week to week lectur

Use Python language for this Assignment. Find the Resources (Week to week lectur

Use Python language for this Assignment. Find the Resources (Week to week lecture) for this Assignment. Avoid Plagiarism. Book: Data Structures and Algorithms in Python Please check the attached zip file for resources –  This assignment is on this topic – ·  Stack and Queue ·  Linked List and Circular List  ·  Optimize merge  ·  Matrix ·  Search Trees ·  Optimizing Storage ·  Map…

[deltainfoteklive]

Different Types of Non Preemptive CPU Scheduling Algorithms

In the world of operating systems, CPU scheduling algorithms play a crucial role in managing and optimizing the allocation of the CPU's processing power. When it comes to non-preemptive scheduling algorithms, there are various types that serve different purposes in ensuring efficient task execution and resource allocation. In this article, we will explore these different types of non-preemptive CPU scheduling algorithms and their practical applications. Introduction to Non-Preemptive CPU Scheduling Algorithms Non-preemptive CPU scheduling algorithms, as the name suggests, do not allow a running task to be interrupted by another task. The CPU remains allocated to a process until it voluntarily releases it or completes its execution. This approach provides simplicity and predictability but may lead to suboptimal resource utilization in dynamic environments. First Come, First Serve (FCFS) Scheduling Algorithm The First Come, First Serve (FCFS) scheduling algorithm is the simplest form of non-preemptive scheduling. It operates by allocating the CPU to the first process that arrives and stays in the ready queue until its completion. However, FCFS can suffer from the "convoy effect" when a long process holds up the execution of subsequent shorter processes. Shortest Job Next (SJN) Scheduling Algorithm The Shortest Job Next (SJN) scheduling algorithm aims to minimize the average waiting time by prioritizing processes based on their burst time. The process with the shortest burst time gets the CPU first, ensuring faster execution for shorter tasks. This algorithm is ideal for scenarios where the length of the tasks is known in advance. Priority Scheduling Algorithm In priority scheduling, each process is assigned a priority value, and the CPU is allocated to the highest priority process. This algorithm allows for effective resource allocation based on the importance or urgency of the tasks. However, it may lead to starvation if lower priority processes are consistently deprived of CPU time. Earliest Deadline First (EDF) Scheduling Algorithm The Earliest Deadline First (EDF) scheduling algorithm assigns deadlines to each process and gives priority to the process with the earliest deadline. It ensures timely execution of tasks with strict timing requirements, making it suitable for real-time systems. However, it requires accurate estimation and prediction of deadlines. Round Robin Scheduling Algorithm The Round Robin scheduling algorithm provides equal opportunity for each process by allocating a fixed time slice (quantum) to each process in a circular manner. This approach prevents starvation and provides fair CPU time sharing among processes. However, it may result in increased waiting time for longer processes. Multilevel Queue Scheduling Algorithm The Multilevel Queue scheduling algorithm categorizes processes into different priority queues, each with its own scheduling algorithm. This approach enables prioritization based on different criteria, such as process type, system level tasks, or user-level tasks. It allows for effective resource utilization based on the nature of the processes. Multilevel Feedback Queue Scheduling Algorithm The Multilevel Feedback Queue scheduling algorithm improves upon the Multilevel Queue algorithm by allowing processes to move between different queues based on their behavior. This adaptive approach enables dynamic adjustment of priorities based on the processes' execution history, allowing for improved resource allocation and fairness. Lottery Scheduling Algorithm The Lottery scheduling algorithm assigns tickets to each process, and a random lottery determines the winner of the CPU time. This approach provides probabilistic fairness, where processes with more tickets have higher chances of winning the CPU. It allows for a flexible distribution of CPU time based on resource requirements or priorities. Fair Share Scheduling Algorithm The Fair Share scheduling algorithm focuses on providing equal opportunity for all users or groups in a multi-user environment. It ensures that each user or group receives a fair share of the CPU time based on their relative resource allocation. This approach promotes equitable resource utilization in shared computing environments. Guaranteed Scheduling Algorithm The Guaranteed scheduling algorithm ensures that each process is guaranteed a minimum amount of CPU time, regardless of competing processes. This approach is beneficial for tasks with specific timing requirements or critical operations. It helps ensure the timely execution of critical processes and minimal disruption in real-time systems. Comparison of Non-Preemptive Scheduling Algorithms When comparing non-preemptive scheduling algorithms, several factors come into play, including CPU utilization, throughput, waiting time, response time, fairness, and real-time performance. Each algorithm has its own strengths and weaknesses, making them suitable for different scenarios based on the specific requirements and constraints. Challenges and Limitations of Non-Preemptive Scheduling Algorithms While non-preemptive scheduling algorithms offer simplicity and predictability, they also have limitations. One limitation is their inability to handle scenarios where tasks with shorter arrival times require immediate execution, leading to potential delays or missed deadlines. Additionally, these algorithms may not adapt well to dynamic workloads or varying priorities. Real-world Applications of Non-Preemptive Scheduling Algorithms Non-preemptive scheduling algorithms find applications in various domains, including real-time systems, industrial process control, multimedia processing, and embedded systems. These algorithms are prevalent in situations where predictable execution, guaranteed resource allocations, and optimized task sequencing are crucial. Conclusion In this article, we explored the different types of non-preemptive CPU scheduling algorithms and their applications. Each algorithm has its own characteristics and suitability based on specific requirements. Understanding these algorithms helps in making informed decisions when it comes to resource allocation and optimizing task execution in the world of operating systems. FAQs 1. What are the advantages of non-preemptive scheduling algorithms? Non-preemptive scheduling algorithms provide simplicity, predictability, and determinism. They ensure that once a process starts executing, it will not be interrupted, leading to fewer context switches and lower system overhead. 2. How do non-preemptive scheduling algorithms handle task priority? Non-preemptive scheduling algorithms prioritize tasks based on their arrival time or assigned priority values. The CPU is allocated to the task with the highest priority or the task that has been waiting the longest in the ready queue. 3. Are non-preemptive scheduling algorithms suitable for real-time systems? Yes, some non-preemptive scheduling algorithms, such as Earliest Deadline First (EDF), are specifically designed for real-time systems. They ensure the timely execution of tasks with strict timing requirements. 4. Can non-preemptive scheduling algorithms adapt to dynamic workloads? Non-preemptive scheduling algorithms are generally not as adaptable to dynamic workloads compared to preemptive scheduling algorithms. They may struggle to handle scenarios where tasks with shorter arrival times require immediate execution. 5. What are the challenges of using non-preemptive scheduling algorithms? One challenge of non-preemptive scheduling algorithms is the potential for delays or missed deadlines when tasks with shorter arrival times are present. These algorithms may also struggle to prioritize tasks effectively in highly dynamic environments. Read the full article