This document discusses the application of soft computing techniques in healthcare. Soft computing is a branch of computer science that uses approximations to find solutions to complex problems. It is tolerant of imprecision and uncertainty. The document outlines several applications of soft computing in healthcare, including medical decision making, diagnosis, analysis of cardiac health and kidney diseases, medical image analysis, prediction of chronic diseases, and patient health monitoring. Soft computing techniques can help address issues around a lack of healthcare resources by providing cost-effective solutions. However, challenges remain around ensuring privacy and security of patient data.
The document discusses linear and nonlinear instruction pipelines. It describes different stages in a linear pipeline like fetch, decode, operand fetch, execute, and write results. It also discusses different types of dependencies like data and instruction dependencies that can occur in pipelines and different solutions to handle them like stalling, forwarding, branch prediction etc. The document further explains the design of nonlinear pipelines using concepts like latency sequence, collision vector, forbidden latencies and provides an example pipeline for multiply and add operations.
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.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
Advanced Comuter Architecture Ch6 Problem SolutionsJoe Christensen
This document contains problems and solutions related to pipelining and superscalar techniques in computer architecture. It discusses speedup factors, efficiency, throughput, and latency for a pipelined processor. It also analyzes the DEC Alpha architecture in terms of scalability and addresses a multiprocessor implementation. Several problems are solved related to reservation tables, collision vectors, state transition diagrams, and determining minimum average latency for pipeline scheduling.
Natural Language Processing in Artificial Intelligence - Codeup #5 - PayU Artivatic.ai
This is workshop presentation for usages for NLP in Artificial Intelligence.
This is prepared by Artivatic Data Labs.
For more info for the detailed product, visit at www.artivatic.com
Shift micro operations & 4 bit combinational circuit shifterMonika Chauhan
This document discusses shift micro operations, which are used for serial data transfer and in conjunction with arithmetic and logic operations. There are three types of shift micro operations: logical shift, circular shift, and arithmetic shift. Logical shift transfers zeros through the serial input, circular shift circulates bits without information loss, and arithmetic shift preserves the sign bit for signed numbers. Shift operations can shift register contents left or right. Hardware implementations typically use multiplexers with data and serial inputs/outputs.
The document discusses amortized analysis, which averages the time required to perform a sequence of operations over all operations. It describes three methods of amortized analysis: aggregate analysis, accounting analysis, and potential analysis. As an example, it analyzes the amortized cost of operations on a dynamic table using these three methods and shows that the amortized cost of insertion and deletion is O(1), even though some operations may have higher actual costs when triggering expansions or contractions of the table.
SOLUTION MANUAL OF COMPUTER ORGANIZATION BY CARL HAMACHER, ZVONKO VRANESIC & ...vtunotesbysree
1) The document provides solutions to problems from Chapter 1 and Chapter 2 of a computer organization textbook.
2) In Chapter 1, it discusses basic computer structure, performance improvement through overlapping operations, and performance comparisons between RISC and CISC processors. In Chapter 2, it covers machine instructions, binary representations of numbers, assembly language programming, and addressing modes.
3) Some of the problems solved include calculating non-overlapped and overlapped execution times, comparing RISC and CISC processors under different clock rates, implementing addition and subtraction in binary, writing assembly code to calculate a dot product, and designing programs that use indexed addressing modes.
The document discusses linear and nonlinear instruction pipelines. It describes different stages in a linear pipeline like fetch, decode, operand fetch, execute, and write results. It also discusses different types of dependencies like data and instruction dependencies that can occur in pipelines and different solutions to handle them like stalling, forwarding, branch prediction etc. The document further explains the design of nonlinear pipelines using concepts like latency sequence, collision vector, forbidden latencies and provides an example pipeline for multiply and add operations.
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.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
Advanced Comuter Architecture Ch6 Problem SolutionsJoe Christensen
This document contains problems and solutions related to pipelining and superscalar techniques in computer architecture. It discusses speedup factors, efficiency, throughput, and latency for a pipelined processor. It also analyzes the DEC Alpha architecture in terms of scalability and addresses a multiprocessor implementation. Several problems are solved related to reservation tables, collision vectors, state transition diagrams, and determining minimum average latency for pipeline scheduling.
Natural Language Processing in Artificial Intelligence - Codeup #5 - PayU Artivatic.ai
This is workshop presentation for usages for NLP in Artificial Intelligence.
This is prepared by Artivatic Data Labs.
For more info for the detailed product, visit at www.artivatic.com
Shift micro operations & 4 bit combinational circuit shifterMonika Chauhan
This document discusses shift micro operations, which are used for serial data transfer and in conjunction with arithmetic and logic operations. There are three types of shift micro operations: logical shift, circular shift, and arithmetic shift. Logical shift transfers zeros through the serial input, circular shift circulates bits without information loss, and arithmetic shift preserves the sign bit for signed numbers. Shift operations can shift register contents left or right. Hardware implementations typically use multiplexers with data and serial inputs/outputs.
The document discusses amortized analysis, which averages the time required to perform a sequence of operations over all operations. It describes three methods of amortized analysis: aggregate analysis, accounting analysis, and potential analysis. As an example, it analyzes the amortized cost of operations on a dynamic table using these three methods and shows that the amortized cost of insertion and deletion is O(1), even though some operations may have higher actual costs when triggering expansions or contractions of the table.
SOLUTION MANUAL OF COMPUTER ORGANIZATION BY CARL HAMACHER, ZVONKO VRANESIC & ...vtunotesbysree
1) The document provides solutions to problems from Chapter 1 and Chapter 2 of a computer organization textbook.
2) In Chapter 1, it discusses basic computer structure, performance improvement through overlapping operations, and performance comparisons between RISC and CISC processors. In Chapter 2, it covers machine instructions, binary representations of numbers, assembly language programming, and addressing modes.
3) Some of the problems solved include calculating non-overlapped and overlapped execution times, comparing RISC and CISC processors under different clock rates, implementing addition and subtraction in binary, writing assembly code to calculate a dot product, and designing programs that use indexed addressing modes.
Deep Learning Interview Questions And Answers | AI & Deep Learning Interview ...Simplilearn
- TensorFlow is a popular deep learning library that provides both C++ and Python APIs to make working with deep learning models easier. It supports both CPU and GPU computing and has a faster compilation time than other libraries like Keras and Torch.
- Tensors are multidimensional arrays that represent inputs, outputs, and parameters of deep learning models in TensorFlow. They are the fundamental data structure that flows through graphs in TensorFlow.
- The main programming elements in TensorFlow include constants, variables, placeholders, and sessions. Constants are parameters whose values do not change, variables allow adding trainable parameters, placeholders feed data from outside the graph, and sessions run the graph to evaluate nodes.
Counting Sort and Radix Sort AlgorithmsSarvesh Rawat
Counting sort is an integer sorting algorithm that works by counting the number of objects that have each distinct key value and using arithmetic to determine the position of each object in the sorted output. It runs in O(n+k) time where n is the number of elements and k is the largest element. It requires an extra array to store counts of each key value. Radix sort is an extension of counting sort that sorts elements based on individual digits by performing counting sort repeatedly on each digit, from least to most significant.
This document discusses computer arithmetic and hardware for signed-magnitude addition and subtraction. It contains the following key points:
1) Computer arithmetic refers to basic operations like addition, subtraction, multiplication, and division performed with operands. It provides examples of signed-magnitude addition and subtraction rules and the hardware used to perform these operations.
2) The hardware for signed-magnitude addition and subtraction includes an A register, B register, complementer, parallel adder, and mode control. It performs the operations by setting the registers and control signals.
3) Algorithms for signed 2's complement addition and subtraction are also presented, showing how numbers are added or subtracted based on their relative magnitudes stored in the registers.
The document discusses the greedy method algorithmic approach. It provides an overview of greedy algorithms including that they make locally optimal choices at each step to find a global optimal solution. The document also provides examples of problems that can be solved using greedy methods like job sequencing, the knapsack problem, finding minimum spanning trees, and single source shortest paths. It summarizes control flow and applications of greedy algorithms.
(1) Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller subproblems and storing the results of already solved subproblems. (2) It is applicable to problems where subproblems overlap and solving them recursively would result in redundant computations. (3) The key steps of a dynamic programming algorithm are to characterize the optimal structure, define the problem recursively in terms of optimal substructures, and compute the optimal solution bottom-up by solving subproblems only once.
This document provides an introduction to a lecture on description logic. It begins with an overview of description logic, including its basic constructs of concepts and relationships. It then discusses the ALC description logic, including its syntax, semantics and examples. Finally, it outlines the reading material and topics that will be covered in the lecture, such as reasoning services in description logic.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
This document provides an introduction to NP-completeness, including: definitions of key concepts like decision problems, classes P and NP, and polynomial time reductions; examples of NP-complete problems like satisfiability and the traveling salesman problem; and approaches to dealing with NP-complete problems like heuristic algorithms, approximation algorithms, and potential help from quantum computing in the future. The document establishes NP-completeness as a central concept in computational complexity theory.
This document summarizes different types of adders used in digital circuits and their VLSI implementation. It describes half adders, full adders, and more complex adders like ripple carry adder and carry lookahead adder. It discusses the working of each adder type through logic diagrams, boolean equations and truth tables. The document aims to simulate and synthesize various adders using VHDL on FPGA platforms to analyze their design parameters and speed performance. It concludes that carry-skip adder is more efficient in terms of speed and area compared to other adder types.
The document discusses dataflow analysis and liveness analysis. It defines liveness analysis as determining which variables are "live" or may be needed in the future at different points in a program. This allows optimizations like register allocation by mapping live variables that do not overlap in time to the same register. The document outlines the formal definition of liveness, including live-in and live-out variables at each node, and provides an algorithm to compute liveness information through a fixed point iteration on the control flow graph.
This document presents information on fuzzy arithmetic and operations. It discusses fuzzy numbers, linguistic variables, and arithmetic operations on fuzzy intervals and fuzzy numbers. Some key points:
- Fuzzy numbers are fuzzy sets with certain properties like being normal, having closed interval alpha-cuts, and bounded support.
- Linguistic variables assign linguistic values like "young" or "old" to numerical variables. They are represented as fuzzy sets.
- Arithmetic operations on fuzzy intervals are defined based on the corresponding operations on their alpha-cuts, which are closed intervals. Properties like commutativity and distributivity are discussed.
- Operations on fuzzy numbers are similarly defined based on the alpha-cuts of the resulting fuzzy
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.
This document presents an overview of the Floyd-Warshall algorithm. It begins with an introduction to the algorithm, explaining that it finds shortest paths in a weighted graph with positive or negative edge weights. It then discusses the history and naming of the algorithm, attributed to researchers in the 1950s and 1960s. The document proceeds to provide an example of how the algorithm works, showing the distance and sequence tables that are updated over multiple iterations to find shortest paths between all pairs of vertices. It concludes with discussing the time and space complexity, applications, and references.
The document discusses algorithms for finding shortest paths in graphs. It describes Dijkstra's algorithm and Bellman-Ford algorithm for solving the single-source shortest paths problem and Floyd-Warshall algorithm for solving the all-pairs shortest paths problem. Dijkstra's algorithm uses a priority queue to efficiently find shortest paths from a single source node to all others, assuming non-negative edge weights. Bellman-Ford handles graphs with negative edge weights but is slower. Floyd-Warshall finds shortest paths between all pairs of nodes in a graph.
The document discusses the physical symbol system hypothesis proposed by Allen Newell and Herbert Simon. The hypothesis states that symbol manipulation is essential to both human and machine intelligence. It claims that a system with the ability to manipulate physical symbols according to formal rules can exhibit generally intelligent behavior. The document provides background on the philosophical roots of the idea. It also gives examples of physical symbol systems, such as formal logic and digital computers. Finally, it discusses some of the contributions and criticisms of the physical symbol system hypothesis in artificial intelligence research.
The document discusses the convex hull algorithm. It begins by defining a convex hull as the shape a rubber band would take if stretched around pins on a board. It then provides explanations of extreme points, edges, and applications of convex hulls. Various algorithms for finding convex hulls are presented, including divide and conquer in O(n log n) time and Jarvis march in O(n^2) time in the worst case.
The document discusses asymptotic notations that are used to describe the time complexity of algorithms. It introduces big O notation, which describes asymptotic upper bounds, big Omega notation for lower bounds, and big Theta notation for tight bounds. Common time complexities are described such as O(1) for constant time, O(log N) for logarithmic time, and O(N^2) for quadratic time. The notations allow analyzing how efficiently algorithms use resources like time and space as the input size increases.
Multiple Disease Prediction System: A ReviewIRJET Journal
This document discusses a study analyzing the use of machine learning techniques to predict multiple diseases based on user-inputted symptoms in a multi-disease prediction system. The system employs predictive modelling and examines symptoms to determine potential illnesses and their likelihood. The study focuses on predicting common diseases like diabetes, heart disease, breast cancer, hepatitis, and kidney disease. It evaluates various machine learning algorithms and their ability to accurately predict these diseases from pre-processed healthcare data.
Soft Computing in Management An Introductionijtsrd
Soft computing can be regarded as a collection of techniques that will enable dealing with practical situations in the same way as humans deal with them, i.e. on the basis of intelligence, common sense, experience, etc. One of such demanding problems is the management of companies. To be successful, managers need to support their decision making process with available state of the art tools and techniques that allow managing data in the most effective way. There are various soft computing methods used in management. These methods include fuzzy logic, neural networks, machine learning, probabilistic reasoning, and evolutionary algorithms. These techniques have been used in practical applications in several management processes. This paper is an introduction on the applications of soft computing in healthcare. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Soft Computing in Management: An Introduction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49288.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49288/soft-computing-in-management-an-introduction/matthew-n-o-sadiku
Deep Learning Interview Questions And Answers | AI & Deep Learning Interview ...Simplilearn
- TensorFlow is a popular deep learning library that provides both C++ and Python APIs to make working with deep learning models easier. It supports both CPU and GPU computing and has a faster compilation time than other libraries like Keras and Torch.
- Tensors are multidimensional arrays that represent inputs, outputs, and parameters of deep learning models in TensorFlow. They are the fundamental data structure that flows through graphs in TensorFlow.
- The main programming elements in TensorFlow include constants, variables, placeholders, and sessions. Constants are parameters whose values do not change, variables allow adding trainable parameters, placeholders feed data from outside the graph, and sessions run the graph to evaluate nodes.
Counting Sort and Radix Sort AlgorithmsSarvesh Rawat
Counting sort is an integer sorting algorithm that works by counting the number of objects that have each distinct key value and using arithmetic to determine the position of each object in the sorted output. It runs in O(n+k) time where n is the number of elements and k is the largest element. It requires an extra array to store counts of each key value. Radix sort is an extension of counting sort that sorts elements based on individual digits by performing counting sort repeatedly on each digit, from least to most significant.
This document discusses computer arithmetic and hardware for signed-magnitude addition and subtraction. It contains the following key points:
1) Computer arithmetic refers to basic operations like addition, subtraction, multiplication, and division performed with operands. It provides examples of signed-magnitude addition and subtraction rules and the hardware used to perform these operations.
2) The hardware for signed-magnitude addition and subtraction includes an A register, B register, complementer, parallel adder, and mode control. It performs the operations by setting the registers and control signals.
3) Algorithms for signed 2's complement addition and subtraction are also presented, showing how numbers are added or subtracted based on their relative magnitudes stored in the registers.
The document discusses the greedy method algorithmic approach. It provides an overview of greedy algorithms including that they make locally optimal choices at each step to find a global optimal solution. The document also provides examples of problems that can be solved using greedy methods like job sequencing, the knapsack problem, finding minimum spanning trees, and single source shortest paths. It summarizes control flow and applications of greedy algorithms.
(1) Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller subproblems and storing the results of already solved subproblems. (2) It is applicable to problems where subproblems overlap and solving them recursively would result in redundant computations. (3) The key steps of a dynamic programming algorithm are to characterize the optimal structure, define the problem recursively in terms of optimal substructures, and compute the optimal solution bottom-up by solving subproblems only once.
This document provides an introduction to a lecture on description logic. It begins with an overview of description logic, including its basic constructs of concepts and relationships. It then discusses the ALC description logic, including its syntax, semantics and examples. Finally, it outlines the reading material and topics that will be covered in the lecture, such as reasoning services in description logic.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
This document provides an introduction to NP-completeness, including: definitions of key concepts like decision problems, classes P and NP, and polynomial time reductions; examples of NP-complete problems like satisfiability and the traveling salesman problem; and approaches to dealing with NP-complete problems like heuristic algorithms, approximation algorithms, and potential help from quantum computing in the future. The document establishes NP-completeness as a central concept in computational complexity theory.
This document summarizes different types of adders used in digital circuits and their VLSI implementation. It describes half adders, full adders, and more complex adders like ripple carry adder and carry lookahead adder. It discusses the working of each adder type through logic diagrams, boolean equations and truth tables. The document aims to simulate and synthesize various adders using VHDL on FPGA platforms to analyze their design parameters and speed performance. It concludes that carry-skip adder is more efficient in terms of speed and area compared to other adder types.
The document discusses dataflow analysis and liveness analysis. It defines liveness analysis as determining which variables are "live" or may be needed in the future at different points in a program. This allows optimizations like register allocation by mapping live variables that do not overlap in time to the same register. The document outlines the formal definition of liveness, including live-in and live-out variables at each node, and provides an algorithm to compute liveness information through a fixed point iteration on the control flow graph.
This document presents information on fuzzy arithmetic and operations. It discusses fuzzy numbers, linguistic variables, and arithmetic operations on fuzzy intervals and fuzzy numbers. Some key points:
- Fuzzy numbers are fuzzy sets with certain properties like being normal, having closed interval alpha-cuts, and bounded support.
- Linguistic variables assign linguistic values like "young" or "old" to numerical variables. They are represented as fuzzy sets.
- Arithmetic operations on fuzzy intervals are defined based on the corresponding operations on their alpha-cuts, which are closed intervals. Properties like commutativity and distributivity are discussed.
- Operations on fuzzy numbers are similarly defined based on the alpha-cuts of the resulting fuzzy
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.
This document presents an overview of the Floyd-Warshall algorithm. It begins with an introduction to the algorithm, explaining that it finds shortest paths in a weighted graph with positive or negative edge weights. It then discusses the history and naming of the algorithm, attributed to researchers in the 1950s and 1960s. The document proceeds to provide an example of how the algorithm works, showing the distance and sequence tables that are updated over multiple iterations to find shortest paths between all pairs of vertices. It concludes with discussing the time and space complexity, applications, and references.
The document discusses algorithms for finding shortest paths in graphs. It describes Dijkstra's algorithm and Bellman-Ford algorithm for solving the single-source shortest paths problem and Floyd-Warshall algorithm for solving the all-pairs shortest paths problem. Dijkstra's algorithm uses a priority queue to efficiently find shortest paths from a single source node to all others, assuming non-negative edge weights. Bellman-Ford handles graphs with negative edge weights but is slower. Floyd-Warshall finds shortest paths between all pairs of nodes in a graph.
The document discusses the physical symbol system hypothesis proposed by Allen Newell and Herbert Simon. The hypothesis states that symbol manipulation is essential to both human and machine intelligence. It claims that a system with the ability to manipulate physical symbols according to formal rules can exhibit generally intelligent behavior. The document provides background on the philosophical roots of the idea. It also gives examples of physical symbol systems, such as formal logic and digital computers. Finally, it discusses some of the contributions and criticisms of the physical symbol system hypothesis in artificial intelligence research.
The document discusses the convex hull algorithm. It begins by defining a convex hull as the shape a rubber band would take if stretched around pins on a board. It then provides explanations of extreme points, edges, and applications of convex hulls. Various algorithms for finding convex hulls are presented, including divide and conquer in O(n log n) time and Jarvis march in O(n^2) time in the worst case.
The document discusses asymptotic notations that are used to describe the time complexity of algorithms. It introduces big O notation, which describes asymptotic upper bounds, big Omega notation for lower bounds, and big Theta notation for tight bounds. Common time complexities are described such as O(1) for constant time, O(log N) for logarithmic time, and O(N^2) for quadratic time. The notations allow analyzing how efficiently algorithms use resources like time and space as the input size increases.
Multiple Disease Prediction System: A ReviewIRJET Journal
This document discusses a study analyzing the use of machine learning techniques to predict multiple diseases based on user-inputted symptoms in a multi-disease prediction system. The system employs predictive modelling and examines symptoms to determine potential illnesses and their likelihood. The study focuses on predicting common diseases like diabetes, heart disease, breast cancer, hepatitis, and kidney disease. It evaluates various machine learning algorithms and their ability to accurately predict these diseases from pre-processed healthcare data.
Soft Computing in Management An Introductionijtsrd
Soft computing can be regarded as a collection of techniques that will enable dealing with practical situations in the same way as humans deal with them, i.e. on the basis of intelligence, common sense, experience, etc. One of such demanding problems is the management of companies. To be successful, managers need to support their decision making process with available state of the art tools and techniques that allow managing data in the most effective way. There are various soft computing methods used in management. These methods include fuzzy logic, neural networks, machine learning, probabilistic reasoning, and evolutionary algorithms. These techniques have been used in practical applications in several management processes. This paper is an introduction on the applications of soft computing in healthcare. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Soft Computing in Management: An Introduction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49288.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49288/soft-computing-in-management-an-introduction/matthew-n-o-sadiku
Machine learning is the field that focuses on how computers learn from data. Today, machine learning is playing an integral role in the medical industry. This is due to its ability to process huge datasets beyond the scope of human capability, and then convert the data analyzed into clinical insights that aid physicians in providing care. Machine learning is a powerful, relatively easy to implement tool with numerous possibilities to enhance medical practice. The applications of machine learning in medicine are advancing medicine into a new realm. Therefore, educating the next generation of medical professionals with machine learning is essential. This paper provides a brief introduction to applying machine learning in medicine. Matthew N. O Sadiku | Sarhan M. Musa | Adedamola Omotoso "Machine Learning in Medicine: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd20255.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/20255/machine-learning-in-medicine-a-primer/matthew-n-o-sadiku
This document discusses the use of artificial intelligence and machine learning techniques for chronic disease detection and management. It provides background on chronic diseases and their impact globally. It then discusses how machine learning algorithms can be used to analyze medical data from electronic health records to predict chronic diseases and suggest treatments. Various studies that have developed models using techniques like decision trees, neural networks, and random forests to detect diseases like cancer, kidney disease and diabetes are summarized. The ability of artificial intelligence to help diagnose chronic diseases earlier and improve healthcare management is also mentioned.
Machine learning approaches in the diagnosis of infectious diseases-a review.pdfSmriti Mishra
This document reviews machine learning approaches for diagnosing infectious diseases. It discusses how machine learning algorithms like logistic regression, K-nearest neighbors, support vector machines, decision trees, naive Bayes, neural networks, and ensemble techniques have been applied to diagnose diseases like tuberculosis, influenza, HIV, dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have limitations related to data quality and availability. Ensemble approaches that combine multiple models may improve performance over single classifiers. Developing models requires diverse, high-quality data that accurately represents reality.
IRJET - Digital Assistance: A New Impulse on Stroke Patient Health Care using...IRJET Journal
1) The document presents a study that uses deep learning algorithms and artificial bee colony optimization to predict stroke using medical dataset features.
2) A neural network architecture is developed to classify patients' risk of stroke based on 13 variables from their medical records, with the artificial bee colony algorithm used to preprocess data and extract meaningful features.
3) The random forest algorithm achieved the highest prediction accuracy of over 88% based on metrics like precision, recall, and F1 score compared to other models like logistic regression, naive bayes, and decision trees.
Predictions And Analytics In Healthcare: Advancements In Machine LearningIRJET Journal
This document discusses advancements in machine learning and predictive analytics for healthcare. It begins with an introduction discussing how technologies like machine learning and artificial intelligence can help researchers and doctors achieve goals faster when integrated with healthcare. The document then reviews literature on challenges with analyzing big healthcare data due to issues like data variety, speed and volume. It discusses different machine learning algorithms that have been used for disease prediction and diagnosis, including decision trees, random forests, bagging and boosting. The methodology section outlines the use of an ensemble approach, combining multiple models to improve overall accuracy. Technologies implemented in this work include Python libraries like Pandas, NumPy and Scikit-learn for data processing and modeling, along with Flask and AWS for web app deployment. The
Transforming Healthcare Industry by Implementing Cloud Computingijtsrd
In the present generation, healthcare has become the foremost imperative sector in todays medicinal eon. The massive private documents, responsive details are kept in a scalable manner. The healthcare industry has become more competitive in the digital world. As a thriving industry, its challenging for doctors to understand the moving technology in the healthcare sector. This also deals with the patient's nursing and maintains their portfolios. The overview of the project depicts a role played by the doctors, patients, management, and resource suppliers by implementing cloud technology in the healthcare industry. The platform was designed and developed for user friendly interactions where patients can connect with the management and doctors at any corner of the world. The peculiarity of the project was to withdraw the pen paper method followed by the sector for ages. Cloud computing CC has played a vital role in the project that helped and managed to store, secure large data files. The features while operating the system were QR codes, generating e mails, SMS text, and free trunk calls. This approach assists on track with each individuals health related documents, henceforward approving with the doctors to access the knowledge throughout the flow of emergency and firmly access policy. Besides the facts, it rescues the lifetime of the patients and mutually helps the doctors figure it out comfortably. The utilization of mobile aid applications may be a dynamic field and has received the attention of late. This development provides mobile technology additional enticing for mobile health m health applications. The m health defines as wireless telemedicine involving the utilization of mobile telecommunications and multimedia system technologies and their integration with mobile health care delivery systems. As well as human authentication protocols, whereas guaranteeing, has not been straightforward in light weight of their restricted capability of calculation and remembrance. Ms. Rohini Kulkarni | Pratibha Gayke "Transforming Healthcare Industry by Implementing Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46455.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/46455/transforming-healthcare-industry-by-implementing-cloud-computing/ms-rohini-kulkarni
This document discusses artificial intelligence (AI) and its applications in biomedical fields. It begins by defining AI and biomedical AI as using algorithms and complex structures to analyze medical data similar to human intelligence. The document then discusses how AI is used in areas like medical imaging for tasks like cancer detection, as well as health monitoring, managing medical records, and diagnostics. It also explores technologies like machine learning and deep learning that power biomedical AI applications. Overall, the document provides a high-level overview of the evolution and uses of AI in healthcare and biomedical fields.
Chronic disease (CD) such as kidney disease and causes severe challenging issues to the people all around the world. Chronic kidney disease (CKD) and diabetes mellitus (DM) are considered in this paper. Predicting the diseases in earlier stage, gives better preventive measures to the people. Healthcare domain leads to tremendous cost savings and improved health status of the society. The main objective of this paper is to develop an algorithm to predict CKD occurrence using machine learning (ML) technique. The commonly used classification algorithms namely logistic regression (LR), random forest (RF), conditional random forest (CRF), and recurrent neural networks (RNN) are considered to predict the disease at an earlier stage. The proposed algorithm in this paper uses medical code data to predict disease at an earlier stage.
A Novel Approach for Forecasting Disease Using Machine LearningIRJET Journal
This document discusses using machine learning models to predict diseases. It analyzes several supervised machine learning algorithms, including Naive Bayes, Decision Trees, K-Nearest Neighbors, Logistic Regression, and Convolutional Neural Networks. The key findings are:
1) K-Nearest Neighbors performed best at predicting kidney disease, Parkinson's disease, and heart disease based on the analyses.
2) Logistic Regression and Convolutional Neural Networks predicted breast cancer and common diseases accurately, respectively.
3) Supervised machine learning algorithms show potential for early disease detection when applied to electronic health data, which can help clinicians and improve patient outcomes.
The Role of Artificial Intelligence in Revolutionizing Healthcare A Comprehen...ijtsrd
A breakthrough era that holds enormous promise for increasing patient care, lowering healthcare costs, and improving overall healthcare outcomes has arrived with the integration of Artificial Intelligence AI in healthcare. This in depth analysis examines the several ways in which AI is transforming healthcare, including diagnosis, treatment, drug research, patient management, and administrative procedures. To lay a strong foundation for understanding AI, machine learning, and deep learning applications in healthcare, the examination begins with clarifying their core principles. It explores how AI might be used to analyze large scale, intricate medical datasets including electronic health records EHRs , medical imaging, and genomes, enabling the early detection of disease, precise diagnosis, and tailored therapy recommendations. Additionally, AI driven technologies like natural language processing NLP have demonstrated considerable potential in extracting important insights from unstructured clinical notes and research literature, supporting clinical decision support and medical research. AI powered robotics and automation have also begun to play crucial roles in rehabilitation and minimally invasive surgery, lowering the invasiveness of operations and speeding up patient recovery. The review emphasizes the efforts that are still being made to create AI driven drug discovery systems that hasten the identification of new treatments and enhance the layouts of clinical trials. By examining trends and patterns in healthcare data, it also examines AIs function in predictive analytics, predicting disease outbreaks, and enhancing population health management. Furthermore, in the context of optimizing healthcare operations and lowering administrative duties, the contribution of AI to administrative tasks such as medical billing, fraud detection, and resource allocation is considered. The review emphasizes the significance of privacy, transparency, and responsible AI deployment while highlighting the ethical and regulatory concerns involved with AI in healthcare. In order to fully realize the potential of AI, it also analyzes potential adoption barriers and the necessity of interdisciplinary cooperation between healthcare experts, data scientists, and legislators. In conclusion, this in depth analysis offers a complete overview of how AI is altering healthcare and provides insights into its present successes and potential in the future. This effort intends to spur innovation, educate stakeholders, and open the door for a more effective, patient centered, and accessible healthcare ecosystem by shedding light on the revolutionary effects of AI on healthcare. Kajal Gohane | Roshini S | Komal Pode "The Role of Artificial Intelligence in Revolutionizing Healthcare: A Comprehensive Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/pa
Predicting disease from several symptoms using machine learning approach.IRJET Journal
This document discusses predicting disease from symptoms using machine learning. It proposes using algorithms like KNN, SVM, NB, DT, RF and LR to build a model for disease prediction. The KNN algorithm achieved the highest accuracy of 98.36% on a dataset containing symptoms and medical histories of 4920 patients with 41 different diseases. The goal is to develop a multi-disease prediction system using machine learning to help doctors make earlier and more accurate diagnoses to improve patient outcomes. Future work will focus on expanding the dataset and improving algorithms to increase prediction accuracy.
The emergence of Internet of things IoT , new computing networking paradigms such as cloud computing and fog computing , cloud computing, and machine learning has revolutionized traditional healthcare and led to the dawn of a new era of smart healthcare. Smart healthcare is a huge market opportunity because it improves lots of lives with the smart health solutions. Stakeholders around the globe are seeking innovative, cost effective ways to deliver patient centered, technology enabled smart health care, both inside and outside hospital walls. This paper provides a primer on smart healthcare. Matthew N. O. Sadiku | Adedamola Omotoso | Sarhan M. Musa1 ""Smart Healthcare: A Primer"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25076.pdf
Paper URL: https://www.ijtsrd.com/home-science/health-and-hygiene/25076/smart-healthcare-a-primer/matthew-n-o-sadiku"
CHAPTER SUMMARY
In the medical profession absolute certainty is a folly, especially for treatment of patients. Any strategy for
possible cure is built on steps upon step and stage upon stage. Computers and Internet access provide a good basis
for improving the procedure(s) for treatment. Refuting the accuracy of the initial intuition of medical staff is well
accomplished by definitive checks and counter-checks by the medical machines. A simple lookup in a
symptoms-database is unacceptable in the modern era of artificial intelligence and global expertise. Medical
machines deliver their finding based on the particular inputs and the results of global diagnostic procedures. When
the machines have access to the patient history and the current symptoms, then the collective diagnostic-expertise of
a large community of medical professionals is deployed to validate its findings. Connectivity to patient data and
access to the diagnostic expertise is supplemented by analysis and interpretation of the ongoing treatments and
their results provides the medical team greater confidence in the effectiveness or treatment. Step by step
estimation of the confidence in analysis and the probability of cure are also provided. Though there is not absolute
certainty, the range of error is successively reduced to near zero level by effectively deploying the most recent tools
and techniques in medicine for most of the ailments.
To some extent, the VLSI design teams have improved their confidence in the manufacture of silicon chips to
almost 100 percent by enhanced analysis programs, modification of the chip designs and then reanalysis of the VLSI
design to reach an infallible (well almost infallible) chips with many millions of transistors. Response of humans
and organs to drugs and procedures are less dependable but it appears that there is room for improvement by falling
back on the rigor and discipline of the systems designers and computer aided medical systems. During the late 20th
century, corporations and auto manufactures experienced poor performance in meeting the realistic requirements for
quality and delivery till the American and German manufacturers (Ford and Volkswagen) introduced the discipline
of mass production. In the medical field, it appears the discipline of medical sciences, deployment of computer,
network technologies, and the insights corporate visionaries need a coherent blending to hasten the innovations still
Application of Deep Learning for Early Detection of Covid 19 using CT scan Im...ijtsrd
Machine learning has a vital role in Dataset Analysis and Computer Vision field. Troubles range beginning dataset segmentation, dataset check to structure from motion, object recognition and view thoughtful use machine learning technique to investigate in a row starting visual data. The incidence of COVID 19 in strange part of the humanity is a most important suffering intended for every one the managerial unit of personality country. India is as well incompatible this extremely rough mission used for calculating the disease incidence along with have manage its improvement velocity from side to side a numeral of stringent events. During my job this paper on predict the corona virus is contain significance or not hand baggage in continuing region support up as health monitoring systems. The increasing difficulty in healthcare creates not as high class as by a mature resident, punishment in lush executive most significant to harmful possessions resting on mind excellence as well as escalate think about expenses. Accordingly, present be a require designed for elegant decision support systems to facilitate tin can approve clinician’s to generate improved data’s care decision. A talented go forward be in the direction of power the continuing digitization of healthcare with the intention of generate unparalleled amount of medical information stored in Patients Health Records PHRs and pair it through contemporary Machine Learning ML toolset intended for medical decision support, along with concurrently, develop the proof stand of current datasets. The datasets are composed at KAGGLE it is an online datasets used my paper work. The specific classifications algorithms are applied in SVM, NB and supervised learning Decision Tree are used. Today, gigantic measure of information is gathered in clinical databases. These databases may contain significant data embodied in nontrivial connections among manifestations and analyses. Removing such conditions from recorded information is a lot simpler to done by utilizing clinical frameworks. Such information can be utilized in future clinical dynamic. Dr. C. Yesubai Rubhavathi | Diofrin. J | Vishnu Durga. S | Arunachalam. R | Eben Paul Richard. S "Application of Deep Learning for Early Detection of Covid-19 using CT-scan Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-1 , February 2023, URL: https://www.ijtsrd.com/papers/ijtsrd52792.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/52792/application-of-deep-learning-for-early-detection-of-covid19-using-ctscan-images/dr-c-yesubai-rubhavathi
Therapeutic management of diseases based on fuzzy logic system- hypertriglyce...TELKOMNIKA JOURNAL
The support systems for assisting clinical decision highly improve the quality and efficiency of the therapeutic and diagnostic treatment in medicine. The proper implementation of such systems can emulate the reasoning of health care professionals in such a way that suggest reasonable decisions on patient treatment. The fuzzy logic system can be considered as one of the efficient techniques for converting a complex decision tree that usually facing the physician into artificial intelligent procedure embedded in a computer program. So many properties in fuzzy logic system that can facilitate the process of medical diagnosis and therapeutic management. In this paper, a system for therapeutic management of hypertriglyceridemia was efficiently realized using a fuzzy logic technique. The obtained results had shown that the proposed fuzzy logic contributes a reliable managing procedure for assisting the physicians and pharmacist in treating the hypertriglyceridemia. Many different hypertriglyceridemia treatment cases showed a perfect matching decision between the standard guidelines and that given by the proposed system.
This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
Classifying and Predictive Analytics for Disease Detection: Empowering Health...IRJET Journal
This document summarizes a research paper that aims to develop a machine learning model using convolutional neural networks to predict various diseases from medical imaging data. The proposed approach involves collecting healthcare data, preprocessing the data, training models like logistic regression and random forests on the data, and evaluating the models' performance on a test set. The best performing models would then be deployed into a web application for medical testing to predict diseases like pneumonia, skin cancer, brain tumors, lung cancer, tuberculosis, and breast cancer from images. The goal is to make disease predictions easily accessible to the general public through a user-friendly interface to help enable earlier detection and improved health outcomes.
Involving machine learning techniques in heart disease diagnosis: a performan...IJECEIAES
Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives.
Similar to Essence of Soft Computing in Healthcare (20)
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
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Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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2. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD49264 | Volume – 6 | Issue – 2 | Jan-Feb 2022 Page 543
Conventional computing or hard computing (HC)
requires an analytical, precisely stated model. Hard
computing is deterministic and precise. Health care
systems, however, are less ideal, highly uncertain,
and stochastic in nature. There is lot of uncertainty
and imprecision involved. Soft computing techniques
have also been applied successfully in healthcare data
for effectively diagnosing diseases and obtaining
better results in comparison to traditional approaches.
These approaches include neural networks,
probabilistic models, evolutionary algorithms,
artificial neural networks, fuzzy logic swarm
intelligence, etc. Figure 2 compares hard computing
and soft computing [5].
OVERVIEW OF SOFT COMPUTING
Soft computing (SC) is a branch of computer science
that resembles the processes of the human brain. It
may also be regarded as a newly emerging
multidisciplinary field. Its main objective is to
develop intelligent machines in order to solve real-
world problems. It differs from the conventional hard
computing as it can handle uncertainty, imprecision
easily. While conventional hard computing is based
on crisp values and binary numbers, SC uses soft
values and fuzzy sets.
Soft computing, also known as a computational
intelligence¸ is based on natural as well as artificial
ideas. It differs from conventional computing that is
hard computing. It is tolerance of imprecision,
uncertainty, partial truth to achieve tractability,
approximation, robustness, low solution cost, and
better rapport with reality. In fact the role model for
soft computing is human mind [6].
Soft computing refers to a collection of
computational techniques in computer science,
artificial intelligence, and machine learning. The
techniques aim to exploit the tolerance of imprecision
and uncertainty to achieve tractability, robustness,
and low solution cost. Its principle components
include:
Expert systems
Neural networks,
Machine learning
Probabilistic reasoning
Evolutionary algorithms
Artificial neural networks
Fuzzy logic
Swarm intelligence
Interactive computational models
These computation methods or technologies provide
information processing capabilities to solve complex
practical problems. Some of these techniques are
illustrated in Figure 3 [7].
APPLICATIONS OF SC IN HEALTHCARE
Soft computing is used for solving real-life problems
and can be applied in different fields such as
education, healthcare, business, industry, engineering,
power systems, transportation, communication
systems, wireless communications, data mining,
home appliances, robotics, etc. [8]. In the healthcare
industry, one wrong decision can result in loss of
lives or permanent damage to the patients. Medical
doctors are increasingly turning to soft computing to
diagnose the patients’ ailments from the symptoms
accurately and avoid wrong diagnosis. Typical
applications of soft computing in healthcare include
the following:
Medical Decision Making: Healthcare
practitioners need to diagnose a disease and make
a decision about the treatments. Patients have
symptoms, which are manifestations of the
disease or a group of diseases. For proper
diagnosis, the corrective treatment involves
identifying the underlying cause of symptoms.
Over the years, researchers from computer
science, mathematics, and medical sciences have
developed intelligent tools for supporting medical
decision making. Modern digital technologies
have allowed several soft computing systems to
be successfully developed and used by healthcare
professionals. In healthcare, decision making has
relied traditionally on rule-based reasoning
systems. Intelligent system based on soft
computing (SC) techniques can help patient and
doctors to express their observations that is
inherently vague. SC techniques can handle such
inputs and deduce some inference. SC not only
helps in analyzing data but it is also very effective
in finding relationship between diagnosis,
treatment and prediction of the result in many
clinical scenarios [9].
Medical Diagnosis: Fast and reliable medical
diagnosis is of vital importance in today’s global
world. For example, SARS or the bird flu are
highly contagious and can threaten the world if
they are not fought immediately and with high
efficiency. It is necessary to quickly and surely
diagnose the disease regardless of where the case
is encountered in the world. Depending on
indicators such as blood pressure and the health
history of the patient, a first diagnosis is compiled
using automated decision support systems [10].
Soft-computing techniques have been proposed to
handle vagueness and imprecision in the
diagnosis process. Soft-computing techniques in
the diagnosis of tropical diseases such as malaria,
leishmaniasis, typhoid fever, schistosomiasis,
3. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD49264 | Volume – 6 | Issue – 2 | Jan-Feb 2022 Page 544
yellow fever, onchocerciasis, lymphatic filariasis,
ebola, chagas disease, chicken pox, African
trypanosomiasis, and dengue. Since traditional
diagnostic techniques could not curb the menace
of tropical diseases, it is high-time soft computing
techniques-which are cheaper, varied, and can
handle fuzzy and confusable problems – should
be employed [11].
Cardiac Health: Based on the heart rate
variability (HRV) analysis, cardiology experts can
make an assessment for both the cardiac health
and the condition of the autonomic nervous
system that is responsible for controlling heart
activity and, consequently, they try to prevent
cardiovascular mortality. An enhanced ECG-
based decision making system can exploit a
collection of ontological models representing the
ECG and HRV feature sets and a fuzzy inference
engine [12].
Kidney Diseases: Kidney failure implies that
one’s kidney have unexpectedly stopped
functioning. Chronic kidney sickness depicts
anomalous kidney function. Treatment may avoid
or delay its progression, either by reducing and
preventing the development of some associated
complications, such as hypertension, obesity,
diabetes mellitus, and cardiovascular
complications. An early intervention can
significantly improve the prognosis. A hybrid
decision support system will allow one to
consider incomplete, unknown, and even
contradictory information, complemented with an
approach to computing centered on artificial
neural networks [13].
Medical Image Analysis: Soft computing
techniques are used in medical image analysis and
processing with real-world medical imaging
applications. This includes image enhancement,
segmentation, classification-based soft
computing, and their application in diagnostic
imaging, as well as an extensive background for
the development of intelligent systems based on
soft computing used in medical image analysis
and processing. The soft computing approaches
include fuzzylogic, neural networks, evolutionary
computing, rough sets, and swarm intelligence
[14].
Prediction Chronic Diseases: The chronic
disease is one of the biggest diseases facing
societies all over the world. The chronic diseases
such as cancer, asthma, heart, and diabetics are
non-communicable diseases (NCD) as compared
with another global disease that is an extremely
serious type of global disease. The World Health
Organization (WHO) has reported the chronic
disease is one of the highest grave diseases that
threaten human life in this world. They illuminate
the behavioral habits from environmental factors
that belong to increasing chronic diseases such as
factors (unhealthy diet, physical inactivity,
tobacco and alcohol use, air pollution, age, and
heredity). A soft computing algorithm can
improve the prediction process [15].
Patient Health Monitoring: Health monitoring
systems integrate health monitoring things like
sensors and medical devices for remotely observe
patient’s records to provide smarter and
intelligent healthcare services. They are becoming
common in for the patients of type geriatric,
dying, long suffering etc. either in the hospitals
and homes. The health monitoring often monitors
blood pressure, diabetes, respiration, body
temperature, food and liquid intake, calories
burnt, oxygen consumption, sleep quality,
medicine remainder, etc. Tracking patient data
from a health monitoring system helps the doctors
to take preventive measures to save the life for a
patient. Various devices like blood pressure
monitor, temperature monitor, diabetes monitor,
heart beat monitor, medicine remainder, etc. may
be connected to the patients. The doctors collect
the data of their patients regularly using these
devices and analyze the data. Using the
computational intelligence and soft computing
methods, the doctors analyze the data and make
predictions. The monitoring system using soft
computing techniques is not only limited to
classification and prediction, it is extended to
other supervised and unsupervised learning
algorithms to monitor, diagnose, and treat the
patients [16].
Infectious Disease Modeling: This is a multi-
disciplinary research activity that has made
significant inroads as a valuable and practical tool
for public health experts and decision makers.
Realistic infectious disease modeling must
incorporate parameters aggregated from disparate
database sources. These data may be incomplete,
imprecise, insufficiently specific, or collated at
varying levels of information granularity. With
the ability to deal with imprecise, approximate,
and vague scenarios, soft computing can play an
important role in expanding the use of these
models. Some soft computing approaches have
been used for infectious disease modeling. The
single greatest challenge with infectious disease
modeling is that models are often developed with
only the modelers in mind and not the public
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health experts. Soft computing based approaches
to infectious disease modeling do not suffer from
this deficiency [17].
Privacy Preservation Electronic Health: One of
the biggest challenges facing healthcare is
protecting the important sensitive data of
electronic health records (EHRs) that are
available over web. The real issues on EHRs is
hiding the sensitive huge data especially stored in
distributed environment and shared between
numbers of stakeholders. It is very important to
eliminate the superfluous data and maintains the
privacy of individual record stored in EHRs. To
construct an effective privacy framework for
EHR’s, fuzzy logic system is applied on different
dataset that are available. In fuzzy logic-based
privacy preservation model, the sensitive
attributes of electronic health records values are
set into five linguistic values such as Low, Very
Low, Middle, High and Very High [18].
BENEFITS AND CHALLENGES
Soft computing methods can adapt themselves
according to problem domain. This makes soft
computing techniques more powerful, reliable, and
efficient. It also makes the soft computing approaches
more suitable and competent for healthcare data [19].
Soft Computing techniques aided by the technological
advancements would undoubtedly curb the shortage
in the availability of proper healthcare.
Implementation of soft computing systems for
medical applications should be supported by a solid
security shield that ensures the privacy and safety of
medical data. There is a noticeable “research divide”
between the universities and the community at large,
which is sending the wrong signals to governments,
the WHO, and other stakeholders. Research results
are buried in the archives of universities with little or
no publicity to the larger community.
CONCLUSION
Soft computing is essentially the study of science of
reasoning, thinking, analyzing, and detecting that
correlates the real world problems to the biological
inspired methods. It is one of the front running
technologies which is defining the future of
computing. Soft computing approaches play a vital
role in solving the different kinds of problems and
provide promising solutions. The approaches have
also been applied in healthcare data for effectively
diagnosing diseases and obtaining better results in
comparison to traditional approaches. Soft computing
approaches can adapt themselves according to
problem domain.
Healthcare organizations should be laying the cultural
foundation today for upcoming technology changes in
the near future. What will be needed in the future is
not just the breakthroughs in technology, but
breakthroughs in creative thinking and the ability of
leaders to think differently [20]. The shortage of
healthcare practitioners and increased demand could
crash healthcare systems in the coming years. More
information about soft computing in healthcare can be
found in the books in [14,21-27] and the following
related journals:
Soft Computing
Applied Soft Computing Journal
Applied Computational Intelligence and Soft
Computing
Journal of Healthcare Engineering
Journal of Soft Computing and Decision Support
Systems
International Journal on Soft Computing
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@ IJTSRD | Unique Paper ID – IJTSRD49264 | Volume – 6 | Issue – 2 | Jan-Feb 2022 Page 547
Figure 1 The key segments of the healthcare industry [3].
Figure 2 Comparing hard computing with soft computing [5].
Figure 3 Soft computing approaches [7]