The Living to 100 Life Expectancy Calculator was developed by Dr. Thomas Perls, the director of the New England Centenarian Study, the largest study in the world of centenarians and their families. It was designed to look at risk factors and provide suggestions for increasing longevity (life-span) and improving the quality of life.
Living to 100 Life Expectancy Calculator (ATTACHED)
The calculator has estimated my life expectancy and suggest ways to increase it.
Write a 2 to 3-page “reflection essay”
Describe your feelings or thoughts about completing the calculator and the recommendations.
Refer to concepts and terminology from your textbook and course contents (use 3 or more gerontology terms such as aging, health status, longevity, life expectancy, risk factors, health disparities, quality of life, life course, centenarians etc.) in your reflection essay. Answer the following four questions.
4a. Were you surprised by the Calculator results (yes or no, and why)? Discuss in detail your results and the recommendations.
4b. After reviewing the recommendations, describe the steps that you can take now to reduce the risk of disease and disability and ensure a high quality of life. Do you plan to make any changes recommended on the “Add years to your life page” to reduce your risk? If so, describe what changes you would like to make, and what obstacles or challenges you foresee. If you do not plan to make the recommended changes, explain your decision.
4c. After completing the Life Expectancy Calculator which social determinants of health question(s) would you recommend adding to the questionnaire and why?
Create your own question(s) with the following list of social determinants of health from Healthy People 2030 (Links to an external site.):
Economic Stability- employment, food insecurity, housing instability, poverty
Education Access and Quality: early childhood education and development, level of education, language and literacy
Health Care and Quality: access to health care, access to primary care, health literacy
Neighborhood and Built Environment: access to foods that support healthy eating patterns, crime and violence, environmental conditions, quality of housing
Social and Community Context: civic participation, discrimination, incarceration, social cohesion
4d. Try to imagine what your lifestyle will be like when you are old (“old” can be whatever age you decide it is). For example, do you expect to still be working or retired, alone or surrounded by family, financially secure, independent, traveling etc.
Unique – This paper considers the utilization of facial acknowledgment advances to forestall wrongdoing. The most well-known advances that are being utilized for security and confirmation intentions are dissected. The Eigenface strategy is the most utilized facial acknowledgment innovation, it very well may be utilized for security and verification purposes. This strategy centers around the parts of the face improvements that are significant for ID, this is finished by disentangling face pictures into huge neighborhood and worldwide 'highlights'. There are numerous ways for law authorization to help them in diminishing the measure of wrongdoing. Four of these ways that utilization facial acknowledgment are: FaceIt, coordinating appearances from live security pictures, face acknowledgment in photos and face acknowledgment from draws. As these advancements are improving quickly, the perils and moral issues likewise must be considered before the innovations can really be utilized in our every day lives. From this paper can be reasoned that facial acknowledgment sets out a great deal of open doors to help forestall wrongdoing. Nonetheless, there are still a great deal of troubles that can cause issues when these methods are utilized in reality. 1. Presentation Consistently a great deal of violations occur. Guiltiness is a major issue everywhere on the world. It is a test to find crooks and Artificial Intelligence could assist with this. The advances that exist these days make it simpler to distinguish people. One of these innovations is facial acknowledgment. These advances can distinguish lawbreakers. Facial acknowledgment calculations can analyze two arrangements of information. At the point when a match has been discovered, an individual could be distinguished. This prompts the accompanying inquiry: "How might facial acknowledgment be utilized to forestall regular wrongdoing?" The examination question is restricted to ordinary wrongdoing, since facial acknowledgment is utilized in a great deal of fields. By regular wrongdoing is implied: burglary, savagery, drug deals, affronts, dangers, imitation, driving under impact, developing hemp, robbery, assault, (serious) misuse and murder. To address the fundamental exploration question a couple of subjects will be looked into. The facial acknowledgment advances which exists and how they work will be talked about. The consequences of what the innovations have brought so far will be assessed. Yet additionally the drawbacks, chances and moral issues of facial acknowledgment innovations will be considered in this paper. Besides the security law will be examined in this paper, with center around the European law. Contemplated every one of these things, there can be offered a response to our primary examination question. 2. Facial acknowledgment advancements I. Which facial acknowledgment innovations exist? A various of facial acknowledgment procedures and strategies are being utilized for security and validation purposes which remembers territories for analyst offices and military purposes. Thusly, the facial acknowledgment procedures can assume a part in forestalling wrongdoing. There are different techniques thought about the two essential errands of facial acknowledgment, for example confirmation and recognizable proof. Check, likewise called confirmation, is introducing a face picture of an obscure individual alongside a case of personality, and afterward determining whether the individual is who he/she professes to be. Recognizable proof, likewise called acknowledgment, is introducing a picture of an obscure individual and verifying that individual's personality by contrasting that picture and an information base of pictures of known individuals. There are even some facial acknowledgment methods that can see feelings. These strategies, just as check and recognizable proof are vital with regards to discovering hoodlums so wrongdoing can be forestalled. A couple of instances of the numerous techniques and calculation that can be utilized inside the field of facial acknowledgment are: Geometric Feature Based Methods, Template Based Methods, Correlation Based Methods, Matching, Pursuit Based Methods, Singular Value Decomposition Based Methods, The Dynamic Link, Matching Methods, Illumination Invariant Processing Methods, Support Vector Machine Approach, Karhunen-Loeve Expansion Based Methods, Feature Based Methods, Neural Network Based Algorithms and Model Based Methods . Later on, perhaps the most realized techniques will be examined in a definite manner. The facial acknowledgment techniques that can be utilized, all have an alternate methodology. Some are more much of the time utilized for facial acknowledgment calculations than others. The utilization of a strategy additionally relies upon the required applications. For example, observation applications may best be served by catching face pictures by methods for a camcorder while picture information base examinations may require static power pictures taken by a standard camera. Some different applications, for example, admittance to top security spaces, may even require the renouncing of the nonintrusive nature of face acknowledgment by requiring the client to remain before a 3D scanner or an infrared sensor. Subsequently, there can be presumed that there can be made a division of three gatherings of face acknowledgment strategies, contingent upon the needed kind of information results, for example techniques that analyze pictures, strategies that take a gander at information from camcorders and strategies that manage other tangible information, similar to 3D pictures or infrared symbolism. Every one of them can be utilized in an unexpected way, to keep wrongdoing from occurring or repeating. ii. How do these innovations work? As recorded above, there exists an extensive rundown of strategies and calculations that can be utilized for facial acknowledgment. Four of them are utilized often and are generally known in the writing, for example Eigenface Method, Correlation Method, Fisherface Method and the Linear Subspaces Method. In any case, how do these facial acknowledgment work? Due to word impediments, just one of those four facial acknowledgment procedures, i.e The Eigenface Method, will be examined. Ideally this will give an overall thought of how facial acknowledgment functions and can be utilized. One of the significant challenges of facial acknowledgment, is that you need to adapt to the way that an individual's appearance may change, with the end goal that the two pictures that are being looked at separate a lot from one another. Additionally natural changes in pictures, such as lightning, must be considered, to have fruitful facial acknowledgment. Consequently from an image of a face, just as from a live face, some yet more unique visual portrayal should be set up which can intervene acknowledgment regardless of the way that, all things considered, a similar face will barely ever shape an indistinguishable picture on progressive events. Our capacity to do this shows that we can infer primary codes for faces, which catch those parts of the construction of a face fundamental to recognize it from other faces. One of the four most popular facial acknowledgment strategies is the Eigenface Method. This strategy centers around the parts of the face improvement that are significant for distinguishing proof. This is finished by translating face pictures into critical nearby and worldwide 'features'. Such highlights could possibly be straightforwardly identified with our natural idea of face highlights, for example, the eyes, nose, lips and hair. Researchers Matthew Turk and Alex Pentland  built up a PC framework for the eigenface approach which functions as following: "In the language of data hypothesis, we need to extricate the important data in a face picture, encode it as productively as could really be expected, and contrast one face encoding and an information base of models encoded similarly." This all occurs in the accompanying introduction activities: 1) Acquire an underlying arrangement of face pictures, additionally called the preparation set. Figure 1: Images of the preparation set  Figure 2: Eigenfaces of the preparation set  2) Calculate the eigenfaces from the preparation set, keeping just the M pictures that relate to the most elevated eigenvalues. These M pictures characterize the face space. As new faces are capable, the eigenfaces can be refreshed or recalculated. 3) Calculate the relating dispersion in M-dimensional weight space for each known individual, by projecting their face pictures onto the 'face space'. After the introduction tasks, there are done more activities to perceive new face pictures. 4) Calculate a bunch of loads dependent on the information picture and the M eigenfaces by projecting the info picture onto every one of the eigenfaces. 5) Determine if the picture is a face at all by verifying whether the picture is adequately near 'face space'. 6) If it is a face, order the weight design as either a referred to individual or as obscure. 7) (Optional) Update the eigenfaces or potentially weight designs. 8) (Optional) If a similar obscure face is seen a few times, compute its trademark weight design and join into the known faces. As referenced before, there is an extensive rundown of strategies that can be utilized for facial acknowledgment. Four of them, i.e Eigenface Method, Correlation Method, Fisherface Method and the Linear Subspaces Method, are the most top choice. Underneath here, you can discover the blunder paces of those four techniques, thought about pictures with close yield or the entire face. Figure 3: Graph and table of the consequence of an analysis with the four most utilized facial acknowledgment procedures  As should be obvious, the Eigenface Method has the most mistakes, and the Fisherface Method the least. You can likewise see that the mistake rate is higher with pictures of close yields faces, contrasted with a full face picture. This shows that it is more diligently for a facial acknowledgment calculation to remember somebody if their face isn't completely appeared in the image and the highlights are consequently not perceived. It likewise helps us to remember the way that facial acknowledgment strategies are not totally precise. Ideally they will turn out to be more exact later on, so e.g wrongdoing can be forestalled quicker and better. 3. Aftereffects of face acknowledgment advances in wrongdoing anticipation There are numerous ways for law authorization to assist them with diminishing>GET ANSWER