In recent years, there has been an increasing interest in grey relational analysis (GRA) and its applications in decision making. GRA is a mathematical approach for dealing with imprecision, uncertainty and vagueness in data. It has been successfully used in a variety of fields such as engineering, medicine, economics and management. GRA was first proposed by Deng in 1981. It has since been developed and extended by many researchers. The basic idea of GRA is to construct a grey relational grade (GRG) to represent the degree of similarity between a given data set and a reference set. The reference set is usually constructed by expert knowledge or past experience. The GRG can be used to compare different data sets and to make decisions. For example, in engineering design, GRA can be used to select the best design from a set of alternatives. In medicine, GRA can be used to diagnose diseases. In economics, GRA can be used to forecast market trends.
2.1. Grey relational grade
The grey relational grade (GRG) is a numerical value that represents the degree of similarity between a given data set and a reference set. It is usually calculated using the following formula: GRG = (1–d/max(d))*100 where d is the Euclidean distance between the given data set and the reference set, and max(d) is the maximum Euclidean distance between any two data points in the reference set.
2.2. Grey relational analysis
Grey relational analysis (GRA) is a mathematical approach for dealing with imprecision, uncertainty and vagueness in data. It has been successfully used in a variety of fields such as engineering, medicine, economics and management. GRA was first proposed by Deng in 1981. It has since been developed and extended by many researchers. The basic idea of GRA is to construct a grey relational grade (GRG) to represent the degree of similarity between a given data set and a reference set. The reference set is usually constructed by expert knowledge or past experience. The GRG can be used to compare different data sets and to make decisions. For example, in engineering design, GRA can be used to select the best design from a set of alternatives. In medicine, GRA can be used to diagnose diseases. In economics, GRA can be used to forecast market trends.
3.1. Engineering design
GRA has been successfully used in engineering design. For example, it has been used to select the best design from a set of alternatives.
GRA has been successfully used in medicine. For example, it has been used to diagnose diseases.
GRA has been successfully used in economics. For example, it has been used to forecast market trends.
GRA is a mathematical approach for dealing with imprecision, uncertainty and vagueness in data. It has been successfully used in a variety of fields such as engineering, medicine, economics and management.
The Global AI Index Report 2022 is a joint effort between Stanford University’s Human-Centered AI Institute and the Center for International Governance Innovation (CIGI). It is the third edition of the report and aims to provide a comprehensive overview of AI development and adoption across the world. The report is based on data from over 130 experts and organizations across 50 countries. It covers a range of topics, including AI research, development, and deployment; AI applications; and the social and economic impact of AI. The report finds that AI is becoming more widespread and is having a profound impact on societies and economies. However, it also highlights a number of challenges that need to be addressed, such as the lack of diversity in the AI field, the need for better regulation, and the need for more public engagement with AI.
AI Hiring Index Report
The AI Hiring Index report 2022 will provide an overview of the AI job market, including:
- The most in–demand AI jobs
- The skills that are most in–demand for AI jobs
- The industries that are most in–demand for AI talent
- The geographical regions that are most in–demand for AI talent
- The salary ranges for AI jobs
Relative Hiring Index by Geographic Area 2021
The relative AI hiring index is a measure of how fast AI talent is being hired compared to the overall hiring rate. A higher relative AI hiring index indicates that AI talent is being hired at a faster rate than the overall hiring rate. Newzeland has hired most AI-skilled employees.
AI Job Postings
There is a great demand for AI jobs as per the AI Indexing report. The most popular AI jobs are in the field of data science, followed by software engineering and then product management. The number of AI job postings in the United States increased from 2020 to 2021, while the number of AI job postings in UK and Singapore decreased. Among these machine learning are the key skills (0.57%) which are in demand followed by artificial intelligence (0.33%), and neural network (0.15%). Autonomous driving has the least demand in skill set which is 0.06%. Among the subset of machine learning, NLP has seen the increase in demand from 2010-2021
What Hiring Agencies’ opinion on AI hiring in 2022
The Randstad Employer Brand Research is the world’s largest study into employer branding. In over 40 countries, employers are asked about their employer brand. The research is based on online surveys among employers and employees. The research shows that
- 46% of employers think that it will be difficult to hire the right talent in 2022.
- 36% of employers think that it will be very difficult to hire the right talent in 2022.
- 18% of employers think that it will be somewhat difficult to hire the right talent in 2022.
- Discusses the WSN clustering general issues
- A literature review on WSN clustering is listed as original content
- Few WSN algorithms are discussed with the relevant code download link
WSN Clustering Issues
Wireless sensor nodes are backed by the sensor’s energy limitation. To resolve the issue several methodologies for data transmission have been formulated. The energy consumption reduction process starts from the very first step of the WSN communication setup. That step is clustering. numerous works by researchers have been published for it. Maximum clustering algorithms by Asian researchers revolve around optimization algorithms for clustering of WSN.
Though the optimization is efficient, yet, its adaption to real-life applications is still a question. The deep learning algorithms require complex hardware due to optimization requirements in network training. Unfortunately, this complex hardware is not available in sensor nodes or if used, a massive battery would have been required. This would have limited their usage in that case. With so many algorithms for efficient clustering for better data transmission with lesser packet delay, packet drop and higher throughput. In this blog at free-thesis.com, we have listed a few most cited papers on WSN clustering algorithms and presented an analytical analysis of those.
Table: Literature Review of research papers on WSN Clustering Algorithms
WSN Optimization Steps
1. Define the Problem
For every Optimization algorithm for WSN, it is of utmost importance to define the problem space in the first place. You can find the template for it at the link below which can be integrated with every optimization algorithm.
2. Identify the tuning variables
The optimization algorithm will ask for the searching space dimension. These are your actual problem variables that need to be tuned. For example: in optimal CH selection, the x-y Coordinates are the tuning variables. So if ‘N’ is nodes then the search space dimension would be 2*N.
N=10; % number of nodes area=[10,10]; % nodes deployment area in meter Trange=2; % transmission range of sensor node in meter nodes.pos=area(1).*rand(N,2);% nodes geographical locations nvars = 2*(N); % search space dimension
3. Define the constraints
The Constraints are those which limit the updated tuning variables during the optimization process. For example, the CH selection problem has the number of nodes as the constraint. The selected CH should be within nodes.
4. Download the Free Hybrid Optimization Code
Once all necessary steps are taken care of, you can use any of the #free Optimization codes from free-thesis.com to get the final optimized values.
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When we talk about deep learning, reinforcement learning is a much amazing branch of it. Without the use of any predefined database. It learns from the application environment and trains its agent to get the maximum rewards for the objective. It’s a trending thesis/research topic these days. Though its roots are buries since 1977 when Temporal difference learning was proposed. Later on, Markov Decision Process proved to be the nail in the modern Deep Reinforcement Learning. The usage of reinforcement learning can be understand by an example.
India is celebrating its 74th Independence Day on 15th August. Independence Day is an annual observance day which is celebrated on every 15th August. India celebrates Independence Day and remembers the sacrifices that our freedom fighters made during the struggle against the British Empire. 15th August is declared as the National and Gazetted holiday to celebrate the independence of our country from the British Empire.
On the fifteenth of August 1947, Pandit Jawaharlal Nehru, who turned out to be the first Prime Minister of Independent India. On that day of 1947, he raised the Indian national flag over the Lahori Gate at the Red Fort in Delhi. And after that, in a similar fashion, every year the national flag of India is hoisted from the same platform by the contemporary Prime Ministers of our country.
Mahatma Gandhi was the leader who guided India towards Independence. India was under the British rule for over 250 years. Gandhi returned to India from South Africa in 1915 at the request of Gopal Krishna Gokhale.
Gandhi’s contribution to the Indian freedom movement cannot be measured in words. He, along with other freedom fighters, compelled the British to leave India. His policies and agendas were non-violent and his words were the source of inspiration for millions.
Gandhi’s Salt March is considered to be a pivotal incident in the history of freedom struggle. At the Calcutta Congress of 1928, Gandhi declared that the British must grant India dominion status or the country will erupt into a revolution for complete independence. The British did not pay heed to this.
A man is but a product of his thoughts. What he thinks he becomesBy Mahatma Gandhi
As a result, on December 31, 1929, the Indian flag was unfurled in Lahore and the next January 26 was celebrated as the Indian Independence Day. Then, Gandhi started a Satyagraha campaign against the salt tax in March 1930. He marched 388 kilometres from Ahmedabad to Dandi in Gujarat to make salt. Thousands of people joined him and made it one of the biggest marches in Indian history.
Many more freedom fighters were playing a crucial role in the history of independent India. Bhagat Singh was born on 28 September 1907 into a family that had been involved in revolutionary activities against the British Raj. This would go on to prove just how true he was to his name in the coming years. Bhagat means ‘bhakt’ or ‘devotee’. From an early age Bhagat was devoted to the cause of his motherland and her freedom.
The tragic death of Lala Lajpat Rai was turning point in Bhagat Singh’s life. The independence movement was in full heat. The Simon Commission was set up to look into the state of Indian constitutional affairs. It left the Indian public outraged and insulted that the Commission which was to determine the future of India, did not include a single Indian member in it. Lala Lajpat Rai who was leading a non-violent protest against the Simon Commission when the Commission visited Lahore on 30 October 1928, got grievously injured when the police under orders from James A. Scott, lathi charged on the unsuspecting crowd. Lalaji died on 17 November 1928.
Singh vowed to take revenge for Lalaji’s murder and joined other revolutionaries, Shivaram Rajguru, Sukhdev Thapar and Chandrashekhar Azad, in a plot to kill Scott. However, in a case of mistaken identity Bhagat Singh and Rajguru shot Assistant Superintendent of Police John P. Saunders on 17 December 1928.
Even after fighting so valiantly and sacrificing his life at the young age of 23 for the freedom of this country. Responding to an inquiry under the Right to Information Act the Home Ministry said that it possesses no record to prove that Bhagat Singh has been declared a martyr. There might be no record to prove him a martyr but that doesn’t take away from his greatness and try as we may we can never really forget the cries of “Inquilab Zindabad!”
Criticism and independent thinking are the two indispensable qualities of a revolutionaryBy Bhagat singh
We should take a stand for what you believe in as per the Mahatma Gandhi protesting in independence of India. Another important lesson we can learn from our freedom fighters like Bhagat singh, Lala Lajpat rai, Chandarshekhar Azad, Subhash chandr Bose and Rani Luxmibai is their absolute believes in the power of team building and team work. It is a lovely and necessary thing to dream, and to dream big, and we would be wise to teach our children the same life lesson. The lesson to draw ultimately from our set of incredible freedom fighters is that plans they drew up in their lives, they had the courage, the will, the strength, and the sheer guts, to execute them. Our free-thesis.com follows principle of freedom fighters like team work, action oriented and innovation in research ideas.
Just like Independence Day is valued by other countries around the world, India also celebrates it with happiness and pomp. Come and be a part of this joy. You’re always welcome.