Type Two Diabetes Among Low - Free Essay Example

Sample details Pages: 9 Words: 2639 Downloads: 1 Date added: 2019/05/08 Category Health Essay Level High school Tags: Diabetes Essay Did you like this example? Worldwide, the United States is known for its large portion sizes, low activity levels, and most especially its fatty, greasy, American food that has foreigners dumbfounded. Overall, compared to Mexicans, U.S populations had greater intakes of saturated fat, sugar, dessert and salty snacks, pizza and French fries, low-fat meat and fish, high-fiber bread, and low-fat milk (Batis 2011). One of the major issues that stems from this (unfortunately, in many states, quite true) stereotype is the rising prevalence of type two diabetes. Type two diabetes has become increasingly common among the U.S population in recent years, and the steadily rising numbers are worrisome; low-income Mexican-American populations are especially at risk for developing type two diabetes (Reynaldo 2005). Certain lifestyle choices and experiences, including food insecurity, less physically demanding jobs, an increase in sedentary recreation, and reduced amount of opportunities for physical exercise in daily life are precipitating factors for Mexican-American folks to become overweight, and eventually develop type 2 diabetes (Reynaldo 2005). Don’t waste time! Our writers will create an original "Type Two Diabetes Among Low" essay for you Create order Obesity and chronic diseases such as diabetes in Mexico can no longer be dismissed as problems that only the upper classes are plagued with. Superfluous and unhealthy diets, excess screen time, and a reluctance to exercise are some of the factors at play when examining the trends of non-communicable diseases such as childhood obesity and type two diabetes. Type two diabetes and obesity are especially prevalent in low income Mexican-American children mainly due to poor lifestyle characteristics, but genetics may also play a role in the rising occurrence of these non-communicable diseases. Both the long-term and short-term health effects of these diseases are myriad and disheartening. Diabetes was once considered an adults-only disease but has recently become increasingly common in children. Between 2011-2012, around 23% of new diabetes diagnoses in children were type 2 diabetes. Until 2001, type 2 diabetes accounted for less than 3% of all newly diagnosed diabetes cases in young people; recent studies show that type 2 diabetes now comprises 45% of those cases (Healthline Media). This disease occurs when the levels of glucose in the blood are too high; blood glucose is the bodys main source of energy and comes mainly from the food you eat. A hormone manufactured by the pancreas, insulin, helps glucose get into the cells to be used for energy. In type 2 diabetes, the body either doesnt make enough insulin or doesnt use insulin well. This leads to excess levels of glucose staying in the bloodstream, and not enough reaching the cells of the body (National Institute 2017). Syndemics is defined as the synergistic interaction of two or more coexistent diseases and resultant excess burden of disease (Clair 2008). The chronic stress that results from poverty, discrimination, and other forms of social suffering contribute to the emergence of ill health while making it more difficult to manage and maintain treatment regimens. A syndemic framework is useful for analyzing the health of marginal groups, such as Mexican-Americans living in low-income communities. Type two diabetes and obesity can be paired with living in areas that are low-income/impoverished as a synergistic effect; the more impoverished a community is, the less resources on nutrition they will have access to due to lack of education, and therefore they are more likely to be afflicted with said diseases (Clair 2008). There are several factors that play a role in the syndemics of obesity and type two diabetes among Mexican-Americans, which are outline further in this paper. There are several risk factors for obesity and type two diabetes, as outlined above. For children affected by type two diabetes, the risk factors are slightly different than when adults contract it. If the child has a sibling or close relative with the condition; if they are of Asian, Pacific Islander, Native American, Latino, or African descent; they show symptoms of insulin resistance; or theyre overweight or obese, then their likelihood of developing type two diabetes increases significantly (Healthline Media). Potential health complications that children with type two diabetes could face later on in life include heart disease and other vascular issues, high risk of developing eye problems, nerve damage, weight control difficulties, high blood pressure, hypoglycemia, and poor kidney function (Healthline Media). Of all the causes known for type two diabetes, being overweight is one of the biggest precipitating factors; overweight children are more likely to have insulin resistance, which leads to the body having a difficult time regulating said insulin (Healthline Media). Genetics also plays a role; if one or both parents have this condition, the likelihood that a child will develop type 2 diabetes is much higher. There are several mutations that have been shown to affect the risk of developing type two diabetes. In general, a mutation in a gene that plays a role in controlling blood glucose levels can increase the risk of developing this disease. The genes that control glucose levels include TCF7L2, which affects insulin secretion and glucose production; ABCC8, which regulates insulin; and CAPN10, which is associated with type two diabetes risk in Mexican-Americans (Winter 2018). These genes also have a part in the production of glucose, production and regulation of insulin, as well as how glucose levels are sensed in the body. The combination of genetic factors and environmental factors make certain populations more at risk than others for developing type two diabetes, but there are ways to combat these statistics, which will be outlined further on in this paper. Food insecurity is one of the major lifestyle factors that plays into the risk of a Mexican-American child developing these diseases. Household food insecurity, defined as the limited ability to acquire nutritionally adequate and safe foods in socially acceptable ways is a growing problem in the United States. Minority groups, especially Latinos, are disproportionately affected by food insecurity; nearly 27% of Latino households experienced food insecurity in 2009 compared to 11% of non-Latino Whites (Fitzgerald 2011). Additionally, acculturation, or the process by which immigrants adopt the attitudes, values, customs, beliefs, and behaviors of a new culture has also been linked to diabetes, diabetes risk factors, and food insufficiency. This is especially an issue presented to low-income Mexican-Americans who come from cultures of eating traditionally Mexican foods to the United States, where the availability of processed, fatty foods is high at every convenience store and fast food restaurant in nearly every city. Recent studies have shown that Latino immigrants arrive in the U.S practicing healthier behaviors than their American counterparts. Acculturation has also been associated with certain lifestyle choices, such as poorer nutrition, an increase in tobacco use, and substance abuse. Thus, it can be argued that the process of acculturation may increase health disparities in Mexican-American populations (Perez-Escamilla 2011). Tied to the issue of food insecurity, low income families often resort to fast food as it is a cheap, easy and quick meal that can feed their entire family. In a study done on Latinas in Hartfod, Connecticut, which interviewed over 200 Latinas about their experiences with food insecurity and type two diabetes, reports that participants with type two diabetes more likely to be obese, and be less physically active but were less likely to consume alcohol or skip meals the diabetes group participants reported lower intakes of non-green leafy and non-starchy vegetables, and regular beverages/sweets, and higher intake of diet beverages/sweets (Fitzgerald 2011). Additionally, it was noted that Latinas with very low food security were 3.3 times more likely to have type two diabetes in comparison to non-Mexican-Americans who were food secure (Fitzgerald 2011). Current analyses show that low nutrition knowledge is associated with greater likelihood of low food security. It is possible that educating low-income communities on maintaining good nutrition could potentially protect households against facing food insecurity. It could also be useful in developing skills to cope with mild food shortages; however, facilitating access to healthy, nutritious foods is essential for households with food insecurity, and seems to be one of the best ways to fight the issues of childhood obesity and type two diabetes. The link between childhood obesity/type two diabetes and economic status is an important one to examine when studying the prevalence of these non-communicable diseases. Individuals with lower income and less education are two to four times more likely to develop diabetes than more advantaged individual (Fox 2013). Michael Fox, in his article on social determinants of health, notes that poverty and material deprivation, defined as a lack of resources to meet the prerequisites for health, may play a key role for disadvantaged individuals, the constant scramble to make ends meet results in high levels of chronic stress, spurring both psychological and biologic responses (Fox 2013). Many can agree that a lack of resources can put immense strain on a person, especially when it comes to feeding themselves and their family. He continues, saying that: Chronic stress can lead to increased depression and anxiety, reduced self-esteem, and decreased energy and motivation, which amplify the likelihood of self-destructive behaviors and choices the physical manifestation of chronic stress leads to the negative consequence of allostatic load, which includes increased blood pressure, cortisol, and blood glucose levels, as well as impaired ability to effectively respond to future stressors. Over time, these physiologic reactions, coupled with detrimental psychological responses, and behavioral practices increase the likelihood of obesity and Type 2 diabetes (Fox 2013). Low-income families often experience high levels of these types of stressors due to their inability to provide adequate food, shelter, or clothing for their families. This issue is all too common in many areas of the United States, adversely affecting Mexican-American families more so than their white counterparts. Rising costs of healthcare also plays into the low-income role. The financial burden of increased health care costs can further intensify the effects of low economic status, particularly due to the fact that it consumes a major portion of income. A low-income individual or family may not have sufficient access to the resources necessary to manage conditions such as diabetes or may not have access to health insurance at all due to their lack of financial resources. Diabetes can decrease an individuals general productivity at work, at school, and in personal leisure time particularly if left unmanaged, which can lead to further employment-related issues. These conditions exacerbate the cycle of inequality, as they lead to further poverty if these disadvantaged individuals are left to fend for themselves with little to no resources to manage their disease (McDonald 2018). Another factor which plays a major role in the rising numbers of type two diabetes in Mexican-American populations is the increase in sedentary recreation, or more specifically, how often these folks are choosing to stay inside instead of getting exercise through outside leisure time. The 2015 New Mexico Youth Risk and Resilience Survey in the largely Hispanic county of Otero done in the Southwestern region of the United States on rural Mexican-American children gave some interesting insight to the habits of play and technology use. The prevalence of obesity is at 26% among Hispanic children and 47% among Hispanic adults; 27.7% of middle-school students (sixth- to eighth-graders) watched 3 hours or more of television, and 28.5% used computers or video games for 3 hours or more on weekdays (McDonald 2018). A large contributor to obesity is sedentary behavior, such as using electronic screen devices. Low-income and racial/ethnic minority children report more time using electronic devices for recreational purposes than do their non-Hispanic white counterparts (McDonald 2018). The overall lack of exercise as well as an increased time spent inside participating in more sedentary activities has led to this drastic increase in the diagnosis of type two diabetes. The overarching question many researchers have been asking is, why does type 2 diabetes affect Mexican-American populations disproportionately more than others? In a study done in South Texas, researchers Daniel Hale and Guadalupe Rupert note that over the 9-year period over which they completed their research, the incidence of diabetes almost tripled, with the majority of that increase being due to the increasing numbers of children with type 2 diabetes (Hale 2006). Of the 669 children with diabetes these researchers observed, 82% were of Mexican-American descent; 66% of the children with type 2 diabetes had one parent known to have diabetes, and 4% of the children had two parents with diabetes (Hale 2006). Mexican-American diets of children in low income areas, as well as parental influence on diets, plays a major role in the rising rates of type two diabetes. The high rates of overweight and obesity among Mexican American children are indicative of the ethnic disparity between Mexican-Americans and non-Latino Whites (Hale 2006). There are significant differences in parental attitudes, beliefs, and practices related to childrens behaviors between Latino and non-Latino populations. Some aspects of Mexican culture i.e. expectations of children to obey their parents and traditional gender roles that assign more childcare responsibility to mothers could also potentially influence Mexican-American mothers to make decisions regarding childrens food choices with little input from anyone else. The reasons for why this chronic disease effects the Mexican-American subpopulations in the United States more adversely than others are varied, and educating the young people on proper nutrition and health is of utmost importance. When these families immigrate to America, they are often faced with competing gender roles, a more child-focused society (i.e. the children get more of a say in their decisions) and the necessity for mothers to work outside the home. These factors may lead to the children being able to have more of a say in their nutritional choices, which could potentially lead to poor snacking habits. In a study done on Mexican-American women, it was shown that the majority of the mothers interviewed were the ones who made the decisions regarding meals. Furthermore, when asked what factors were most important to them when selecting breakfast foods, participants most frequently said they chose foods because their child liked them, wanted them, and would eat them (Davis 2017). Often times, children will choose foods that tend to be unhealthy, because this is generally the type of food that appeals to young people. With the choice in their hands of what to eat, allowing children to choose their own breakfast foods could be a contributing factor to the rising statistics of childhood obesity and type two diabetes diagnoses. As mentioned previously, low income families who have low food security can often turn to unhealthy, inexpensive foods to feed their family, which comes with these negative health risks. In order for the rising numbers of childhood obesity and type two diabetes to be lowered, several things must be done. First, increased education on nutrition needs to be readily and widely available to all communities, regardless of social class or economic status. It needs to be accessible and easy to understand for all populations; i.e., if there is a community that is predominately Spanish-speaking, there needs to be information available in Spanish as well as in English. Second, the information needs to be applicable to the daily lives of these afflicted individuals. A government agency entering into a population and showing them a picture of what a nutritious, portioned plate should look like will not do any good if the foods that are shown are not foods known to these communities. The information must be presented in a way that enables the folks in these populations to understand and be able to correctly implement these new strategies into their lives. Lastly, educating children and promoting healthy lifestyles in parents and children alike will be principally important in overcoming the rising prevalence of diabetes. If children see their family members exercising regularly and taking proper care of themselves, they are much more likely to follow suit. In summary, type two diabetes and obesity are two chronic noncommunicable diseases that are especially prevalent in low income Mexican-American populations. This is mainly due to poor lifestyle characteristics that are exacerbated by lack of financial resources as well as the poor nutritional education provided to said communities. Many researchers agree that nutritional education is the key to fighting these chronic diseases and lowering the rates of diagnoses.

Direct Current (Dc) Motors . Student’S Name. University.

Direct Current (DC) Motors Student’s Name University Course Code Date of Submission Direct Current (DC) Motors 1.0 Overview of DC Motors A Direct Current (DC) motor is a broad classification of electric motors that operate from a direct current (DC). Nearly all mechanical devices used in electric applications are powered by electric motors. Motors transform electrical energy to mechanical energy. Thus, they are essential for energy conversion in electrical machines. All types of electrical motors comprise a stationary field commonly referred to as stator, and a rotating field (or rotor). According to Krishnan, (2010), the mechanism of operation of DC motors is based on the interaction between electric current and the†¦show more content†¦Rizzoni (2004) asserts that when electrical energy is supplied to the windings perpendicular to the direction of the magnetic field, the flowing current and the magnetic field will interact. As a result, mechanical energy will be produced by the magnetic field, leading to motion. Fleming’s Left Hand Rule can be used to determine the direction of the motion Figure 1: Fleming’s Left Hand Rule (Rizzoni, 2004) The direction of both the magnetic field and the current are pointed by the first and second fingers respectively. The thumb indicates the direction in which the conductor is pushed by effects of the magnetic field (Rizzoni, 2004). 1.3 Operation Principles of a DC Motor In order to demonstrate the operating principles of a DC motor, this paper will focus on a coil subjected to a magnetic field with a flux density of B (Hambley et al., 2008). When a D.C voltage is supplied to the coil, it induces current to flow through the coil. The electric current and the magnetic field will therefore interact and create a force that pushes the coil to move in the direction shown in Figure 2 below. Figure 2: Torque production in a DC motor (Hambley et al., 2008) A practical DC motor comprises several coils wound on the rotor as shown in Figure 1. The coils are aligned in the direction of the force that induces them to rotate. 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Implementation of Artificial Intelligence at Woolworths Limited

Question: Discuss about the Implementation of Artificial Intelligence at Woolworths Limited. Answer: Introduction: Artificial intelligence has showcased exponential development in the past two decades capitalizing upon the development in computer science, operations research, nanotechnology and neuroscience. The convergence of several fields with an technology driven approach has presented with overwhelming possibilities in terms of benefits derived by end users along with high growth potential as an business opportunity. Woolworths Limited has the largest market share in retail sector of Australia with encapsulating nearly 80% of retail consumer base. Global retail giants such as Amazon tends to emphasize upon numerous sets consumer based researches in order to improvise upon the deliverables to be offered to its online visitors. The implementation of artificial intelligence (AI) at Woolworths will improvise upon the companys comparative advantage both in terms of retention of Australian retail market along with overseas consumer base. The case study seeks to highlight different sets of AI applications that can potentially benefit the company in terms of improving retail experience for its consumers. The current sets of applications deployed by the company focuses towards presenting products online to a consumer based upon his/her sales history. However, AI can add numerous dimensions towards such recommendations through undertaking sentimental analysis and shifts in trends to come up with relevant searches. Problems addressed by Artificial Intelligence: The online retail experience of consumers tends to get affected owing to a large number of product alternatives available along with different sets of variant in each product segment. The paradox of choice results in aggravating of confusion of prospective consumers about to get their initial sets of retail shopping. Moreover, the search engines are not fine tuned in order to provide search results based upon consumers preferences. Consumers seek assistance progression of the kind that are not fully addressed by the present sets of measures available at Woolworths online retail sites. The marketing campaigns undertaken by the company currently employs large number of employees entrusted with promotional activities or outreach programs for its consumers. Moreover, the prevalence of non alignment between the actual tangible form of the product and its online representation through photographs and product details results in high rate of dissatisfaction amongst aggrieved clients. This is owing to the fact that such non alignment results towards the products not reaching the initial benchmark that has been set up by the clients. In terms of search engine optimization process inside the Woolworthss online stores, the company has to improvise upon bringing out relevant searches based upon the search term entered by the consumer coupled with his/her browsing history. AI can improve the search processes through personalized searches for each consumer that equates with their previous transactions with Woolworths Limited. In case of brick and mortar stores by Woolworths, the dissemination and communication of different sets of offers provided by the company towards targeted consumers requires precision in terms maintaining databases. AI can have positive implications both in terms of augmenting the consumer experience and streamlining the degree of sustainable growth in revenue for the company through error free maintenance of different sets of company operations. Potential benefits that can be derived using Artificial Intelligence: From the initiation of product search in the retail website to resolving of issues pertaining to logistics and delivery of such products to its end users, AI can function upon numerous set of parameters. Moreover, in order to optimize the transactions made by the consumers and assist the company towards improving its revenue generating capabilities through elevating the degree of customer satisfaction, the role of AI tends to be crucial. Through deployment of machine learning algorithm based programs that evolves based upon the data sets made available to it, better sets of predictions as regards to changing consumers preferences and online activity can be undertaken. Wenger (2014) states that machine learning programs are not application specific and tends to modify itself in accordance with the data made available to it and analyses the data structure in order to decide upon the degree of optimization. Artificial intelligence, through Reinforcement Learning can also contribute towa rds upgrading the security profile of the online site of Woolworths through heightening the degree of surveillance on suspicious activities on its network. Figure 1: Machine Intelligence (Source: Celent.com, 2016) Online retailing encompasses dissemination of sensitive financial information while putting forward a transaction for billing purposes. Thereby, the bank and credit card details has a significant probability of getting compromised with the advent of sophisticated malware. Artificial intelligence optimizes the existing security to such an extent through multilevel encryptions that malware and unwarranted programs ceases to persist (Bond Gasser, Eds, 2014).Moreover, through constant evolving of data functions and detailing of operational and infrastructural problems based upon the consumer grievances, help desk incidents and computer logs AI can significantly reduce probability of potential threats. AI has the potential to replace human shopping consultant through customizations of product options based upon the consumers behavioral analytics, sensitivity towards the different sets of products offered and up-gradations regarding emerging consumption trends. In terms of recruitment of employees by Woolworths, AI can improvise upon short listing of candidates based upon pre-programmed metrics such as previous work experience, reasoning skills, experience in sales. Further, due to the quantum of training procedure involved for sales staff along with progressive sets of skill acquired by an employee, turnover costs remains high. Implementing AI in recruiting reduces the probability of employee turnover owing to optimizing the selection process (Seibt, Hakli Nrskov, 2014).. Supply chain management remains an issue even for the global leaders in retailing, through algorithmic SCM technologies using data from machine learning both the inventory management and distribution can be automated in tune with the sales forecasts. Further, the stock out rates along with backorders that tends to affect the consumer retention rate significantly can be minimized efficiently through AI based forecast modeling. The effectiveness of different sets of market ing strategy can be gauged with precise sets of event timings and keywords that were used for such marketing campaign using machine learning techniques . Implementation of data modeling procedures in order to compute sales and revenue returns from different sets of discount offerings and promotional events pertaining to Woolworths and its market competitors can be undertaken using predictive analytics tools. Figure 2: Life cycle of AI installation (Source: Bond, A. H., Gasser, L. Eds., 2014) In case of decision making with regards to opening up of stores, data regarding demographics, income distribution amongst the local population, proximity to community events and proximity to nearby competitors can be evaluated by AI algorithmic game theory and data modeling. The recent success of AlphaGo, an AI developed by Google, over a professional player in a five match strategy game showcases the fact that AI can excel through Reinforced Learning and even excel human in terms of strategizing skills (News Blog | DeepMind. 2016). Thereby, implementation and developing a separate AI division that caters to different sets of retail management, supply chain configuration and customer relationship management through Deep Learning, Reinforced Learning and Natural Language Processing is imperative for Woolworths. Strategic plan: The initiation of implementing AI based technology requires reconfiguration of online metrics to facilitate merging of AI based resources onto the site metrics. The initial alignment between AI and the online site requires installation of additional sets of servers that tends to accommodate addition sets of random access memory reconfiguration. The high growth prospects in artificial intelligence along with the applicability in terms of collecting and synthesizing behavioral analytics data results towards exponential augmentation of revenue generating capability. The investment scaling taking into consideration the demand and sales estimates can be implemented through AI applications that will seek to evaluate probable constraints to such escalations. Moreover, the scaling of information network security through implementing machine learning into the system servers will aggravate the detection rate of malware previously undetected into the system. Figure 3: AI installation into corporate lifecycle (Source: News Blog | DeepMind., 2016) The company should initially undertake installation of IBM Watson on to its networks, prior to which it requires to recalibrate its existing IT infrastructure. The installation of bandwidth extension has to be initiated for peripherals coupled with expansion of cloud based services in order to provide the basis augmentation. The recruitment of system engineers and computer science doctorates on to the AI team shall facilitate towards dissecting different the underpinnings of neutral networks that tends to operate the AI. Further, only cost minimization of existing facilities cannot be the sole source of covering installation costs. There ought to be a source of cash inflow in order to further capitalize upon the RD and AI expenditures that are to be incurred by the company. Figure 4: Global revenue forecasts from Artificial Intelligence (Source: Artificial Intelligence for Enterprise Applications | Tractica. 2016) Thereby, the company should outsource its cloud based services onto other minor companies that are technology driven but do not have the adequate infrastructure to utilize the latest developments in the field of Artificial Intelligence. For instance, the company may seek to provide AI services to the different sets of transportation and healthcare companies for which AI system can provided larger breakthroughs in terms of providing cutting edge healthcare facilities coupled with improvement in the success rate of surgeries followed up gradation of transport routes or streamlining the logistical system in order to facilitate better returns for both the companies. Moreover, the insights gained from such a large consumer bases that Woolworths presently encapsulates in the 14 different economies that the company operates in. The company, owing to high degree of market exposure, can improvise towards providing highly valuable behavioral, preference and consumption patterns to different se ts of consumables and beverage manufacturers. Further, such insights can also be valuable towards providing highly consumer driven AI inputs. The above table showcases the fact that Asia Pacific region- the region where the majority of Woolworths business operations are undertaken has been forecasted to generate highest revenues by providing AI services. Thereby, Woolworths can capitalize heavily upon its access to consumers decision making metrics in order to facilitate cash inflows into the project. Project Analysis: CoCComputation of Total Expenditurempu Particulars A$ ('00) IBM Watson Installation 20,000 Development of AI Prototype 65,000 Recruitment of Software Engineers for AI Division 20,000 Development of existing IT Infrastructure 35,000 Expansion of cloud computing services 18,000 Installation of machine learning peripherals 7,500 Total Expenses 165,500 Table 1: Computation of initial cash flow (Source: As created by the author) Computation of NPV Cash Flow Cumulative cash flow Disccunted cash flow Year 0 -165500 1 Year 1 42000 (123,500.00) 0.892857 37,500.00 Year 2 47000 (76,500.00) 0.797194 37,468.11 Year 3 53000 (23,500.00) 0.71178 37,724.35 Year 4 64000 40,500.00 0.635518 40,673.16 Year 5 72000 112,500.00 0.567427 40,854.73 194,220.36 NPV 28,720.36 Table 2: Computation of Net Present Value (Source: As created by the author) The overall table mainly helps in depicting the NPV of the project, which might be helpful in making adequate investment decisions. In addition, the NPV of the project is mainly derived at 28720.36, which mainly indicates the efficiency of the project. In this context, Seibt, Hakli Nrskov (2014) mentioned that with the help fop NPV companies are able to determine the current value of the future cash flows by eliminating the time gap. On the other hand, Glymour, Scheines Spirtes (2014) criticizes that NPV valuation mainly loses its friction during an economic crisis, where the income projected from a project could be reduced and hamper liquidity of the company. Furthermore, the discounting factor is mainly taken at 12%, which helps in depicting the adequate income, which helps in discounting the cash inflows generated from the project. In addition, the overall cumulative cash flow mainly presents that at the fourth year the investment money will be collected by the company. Furthermore, the main cash inflows are mainly projected from the data sales that will be conducted to the retailers regarding the purchase behavior of their customers. This software might help in generating higher predictive analysis for the retailers, which in turn might reduce the excess storage of inventory conducted by retailers. Conclusion The application of artificial intelligence in retailing operations can be multifaceted with the presence of different approaches presented through Machine Learning, Language Processing and Retention Learning. The different sets of approaches resolves issues pertaining to separate domains of the retail operations. The presence of large consumer base tends to bring newer sets of challenges in the realm of customer retention and CRM activities. The different sets of issues with regards to network security, security in terms of transactions and the issues pertaining to supply chain bottleneck followed by logistical delays can be mitigated through the process optimization activities undertaken by AI. Moreover, Machine Learning evolves over time in order to improvise upon the data sets provided in terms of consumer preferences that it could utilize over time to mitigate the different sets of activities. The company can provide the insights derived from continuous data up-gradation with regards to changing consumer preferences to facilitate better access to AI driven data that minor or small scale companies do not have the means to procure. Thereby, the cash inflow through sourcing of such insights lead towards resolving the issue pertaining to installation costs. The overall NPV of the project is mainly depicted at 2872036, which mainly helps in evaluating the viability of the project. 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