find and read an article (newspaper, news magazine, internet article or journal) about a perceived discrimination incident or case in the United States regarding women and society, age, race & ethnicity, immigration, the LGBT population, religion, or those with disabilities. The article must be from the past year (August 2019-September 2020). Examples: Undocumented immigrants; gender equity in salaries and promotions in the workplace; age discrimination in medical or work situations, non-compliance to the ADA Act, civil rights violations, religious rights violations, etc.
The paper should be two to three pages double spaced APA format and cover all areas listed below. Be sure to properly cite your source for the article both within your paper and at the end in the reference list. MUST PROVIDE A LINK (this can be in your reference section).
Does your discrimination in American society response do the following?
Identifying information: Author, date, publication.
Article is from the last four months, addresses given topic and is attached or a link is provided. (1 point)
Short Summary of the article (no more than two paragraphs) What issue does the article address? (4 points)
Agreement/disagreement and strengths/weaknesses with the article. Be sure to note the reasons why you agree or disagree for full points. Were all sides presented? (three to four paragraphs) (6 points)
If you could make a policy to address the discrimination, what would it be? Is there a policy that addresses this issue already? (three to four paragraphs (6 points)
offer optimal performance of the model. Next, we generated a curve demonstrating accuracy of our model’s training and testing set classification over 1,000 iterations (Figure 2). Figure 2. NN feed-forward accuracy over 1,000 iterations of the Simulated Annealing algorithm. The curve generated for our Simulated Annealing model looks strikingly similar to that generated using Randomized Hill Climbing; the algorithm runs into the same delayed reaction and accuracy anomalies, though at different iterations. This can be attributed to the algorithm’s similarities to Randomized Hill Climbing, as well as evidence that interactions of the different attributes within the neural network’s hidden layer are largely responsible for the behavior. Running 1,000 iterations of the Simulated Annealing algorithm lasted 19.56 seconds, placing the algorithm among the fastest within randomized optimization. Looking forward, the accuracy would likely converge higher if the algorithm given more iterations to run. Aside from this, the accuracy could be measured marginally higher by averaging results over multiple trials, or performing a grid search with smaller steps to find a more optimal hyperparameter combination. Ultimately, although achieving slightly higher accuracy than Randomized Hill Climbing, the Simulated Annealing algorithm still falls fall short of backpropagation impressive 97.38% testing accuracy. Genetic Algorithm The Genetic Algorithm applies biological principles to optimization problems; using a system of mating populations in order to find the fittest member of a search space. From a high level, the algorithm encodes relevant information of a search space into ‘chromosomes’ of population individuals, and randomly crosses-over these chromosomes over each generation using ratios encoded as constants. Through each generation, the best members are carried over and the worst are discarded. Over enough iterations, the population will contain optimal members of the search space. Three important hyperparameters are necessary in order to fine-tune a Genetic Algorithm optimization model: the mutation count, or the number of individuals who have their chromosomes spontaneously mutated, the mating count, or the number of individuals who mate to produce offspring, and the population size, or the number of individuals to maintain each generation. Too many mutating individuals can prevent the algorithm from maintaining a genetically superior population. These mutations are crucial, however, as they prevent the algorithm from halting at local minima by applying randomness. As such, we will confine the mutation count to be no more than ¼ the population size. Constraining our population size to be 200, we perform a grid search on the mating and mutation count to find an optimal set of hyperparameters (Table 3). Like before, we use a grid search with a 70/30 train/test split.>GET ANSWER