Applications of Data Mining Techniques in Airline Industry Essay Example

Applications of Data Mining Techniques in Airline Industry Essay Purpose and Scope All around the universe, the air hose industry could be described in few words, which is intensely competitory and dynamic . The air hose industry generates one million millions of dollars every twelvemonth but still has a cumulative net income border of less than 1 % 1. Many Airlines are seeking to retrieve from deep debt. The grounds for these are multifold- fuel monetary values, high cyclicality and seasonality, ferocious competition, high fixed costs and many other issues related to security and riders safety. To guarantee for the best economic result, Airline companies are seeking with their most originative plus information. Datas used in concurrence with informations mining techniques allows comprehensive intelligent direction and decision-making system. Achieving these benefits in a timely and intelligent mode may assist in ensuing lower operating costs, better client service, market fight, increased net income border and stockholder value addition. This intent of this paper is to show the applications of informations mining techniques on multiple facets of air hose concern. For illustration, to foretell the figure of domestic and international air hose riders from a specific city/airport, to dynamically monetary value the tickets depending on seasonality and demand, to research the frequent circular database to fix for CRM execution, to makes the operational determinations about catering, forces, and gate traffic flow, to help the security bureaus for secure and safe flights for the rider specially after 9/11 incident. Predict the Number of Passenger by using Data Mining Technique We will write a custom essay sample on Applications of Data Mining Techniques in Airline Industry specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Applications of Data Mining Techniques in Airline Industry specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Applications of Data Mining Techniques in Airline Industry specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Prediction is critical to any concern for planning and gross direction, particularly in the Airline industry, where a batch of planning is required to buy/lease new aircrafts, to engage crew members, to happen the new slots in busy airdromes and to acquire the blessings from many air power governments. In the instance of Air travel, batch of seasonality and cyclicality involved. Passengers are more likely to wing to some finishs based on the clip of the twelvemonth. Business travellers are likely to go weekdays than weekends. Early forenoon and eventide flights are desired by concern travellers who want to carry through a twenty-four hours s work at their finish and return the same twenty-four hours. To calculate the figure of rider, unreal nervous web ( ANN ) can be used. The intent of a nervous web is to larn to acknowledge forms in a given informations. Once the nervous web has been trained on samples of the given informations, it can do anticipations by observing similar forms in future informations. The growing factors which might act upon the air travel demand depend on several things. Mauro Calvano2 in his survey of conveyance Canada air power prognosis 2002-2016 considered 12 major socio-economic factors as follows: GDP Personal Disposable income Adult Populations US economic Mentality Airline Yield Fleet/route structure/Average Aircraft Size Passenger Load factors Labor cost and productiveness Fuel cost/Fuel efficiency Airline cost other than Fuel and Labor Passenger Traffic Allocation Assumptions New engineering Factors 1 to 5 are related demand side of the prognosis Factors 6 to 10 are related to operations and supply side Factors 10 and 11 represent the structural alterations This historical information is called the appraisal set. A fraction of the overall available information is reserved for formalizing the truth of the developed prognosis theoretical account. This reserved information set is called the prediction set because no information contained in it is used in any signifier during the development of the prognosis theoretical account. The information in the prediction set are used for proving the true extrapolative belongingss of the developed prognosis theoretical account. The appraisal set is farther divided into a preparation set and a testing set. Information in the preparation set is used straight for the finding of the prognosis theoretical account, whereas information in the testing set is used indirectly for the same intent. Figure1: Forecasting Process Model For a given ANN architecture and a preparation set, the basic mechanism behind most supervised acquisition regulations is the updating of the weights and the prejudice footings, until the mean squared mistake ( MSE ) between the end product predicted by the web and the desired end product ( the mark ) is less than a pre-specified tolerance. Nervous webs are can be represented as beds of functional nodes. The most general signifier of a nervous web theoretical account used in prediction can be written as: Y = F [ H1 ( x ) , H2 ( x ) , aˆÂ ¦ . , Hn ( x ) ] + U Where, Y is a dependant or end product variable, Ten is a set of input/ influencing variables, F A ; H s are web maps, and U is a theoretical account mistake. This input bed is connected to a concealed bed. Hs are the concealed bed nodes and represents different nonlinear maps. Each node in a bed receives its input from the predating bed through nexus which has weights assigned, which get adjusted utilizing an appropriate acquisition algorithm and the information contained in the preparation set. Figure2: ANN Architecture Abdullah Omer BaFail3 did the survey to calculate the figure of air hose rider in Saudi Arabia. He selected the most influencing factors to calculate the figure of domestic riders in the different metropoliss of Saudi Arabia. For Dhahran he selected factors like: Oil gross domestic merchandise for last 6 old ages, private non-oil gross domestic merchandise, Import of goods and services for last 10 old ages, and population size for last 2 old ages. The domestic and international existent and forecasted figure of riders for the metropolis of Dhahran for the old ages 1993 through 1998 is shown below. Prognosiss underestimated the existent travel. The Mean Absolute Percentage Error ( MAPE ) for domestic travel is about 10 % , while for international travel is about 3 % . Figure3: Forecasting consequences from Abdullah Omer BaFail3 The take away from the Abdullah Omer BaFail3 for me is that the efficient prediction theoretical account can be invented utilizing ANN if we utilizing the right influencing indexs. In this survey some indexs which influence are oil gross domestic merchandise and per capita income in the domestic and international sectors. In position of the fluctuating nature of the rider use of air hose services in Saudi Arabia, certain suggestions were made. Most of these recommendations were in order to better the flexibleness of the system to the fluctuations in demand and supply. Hub and spike theoretical account was besides suggested as solutions in certain sectors to increase the flexibleness in seting their capacity allotments across markets as new information about demand conditions become available. Application of Data Mining technique to foretell the Airline Passengers No-show Ratess Airlines overbook the flights based on the outlook that some per centum of engaged riders will non demo for each flight. Accurate prognosiss of the expected figure of no-shows for each flight can increase air hose gross by cut downing the figure of perishable seats ( empty seats that might otherwise hold been sold ) and the figure of nonvoluntary denied embarkation s at the going gate. Typically, the simplest manner is to travel for mean no-show rates of historically similar flights, without the usage of passenger-specific information. Lawernce, Hong, Cherrier4 in their research paper predicted the no-show rates utilizing specific information on the single riders booked on each flight. The Airlines offer multiple menus in different booking category. The figure of seats allocated to each booking category is driven by demand for each category, such that gross is maximized. For illustration, few seats can be kept on clasp for the last-minute travellers with high menus and figure of seats sold in lower-fare categories earlier in the engagement procedure. Footings and conditions of cancellation and no-show besides vary in each category. The no-shows consequences in lost gross if the flight departs with empty seats that might otherwise hold been sold. Near accurate prognosiss of the expected figure of no-shows for each flight are really much desirable because the under-prediction of no-shows leads to loss of possible gross from empty seats, while over-prediction can bring forth a important cost punishment associated with denied embarkations at the going gate and besides make client dissatisfaction. In the simplest theoretical account, the overbooking bound is taken as the capacity plus the estimated figure of no-shows. Engagements are offered up to this degree. No-shows Numberss are predicted utilizing time-series methods such as taking the seasonally leaden traveling norm of no-shows for old cases of the same flight. Figure4: No-show tendency over yearss to departure Beginning: Lawernce, Hong, Cherrier4 The simple theoretical account does non take history of specific features of the riders. Lawernce, Hong, Cherrier4 in his survey used categorization method, likewise Kalka and Weber5 at Lufthansa used initiation trees to calculate passenger-level no-show chances, and compared their truth with conventional, historical-based methods. I tried to sum up Lawernce, Hong, Cherrier4 attack and consequences briefly below. Whenever a ticket is booked the Passenger Name Records ( PNRs ) is generated and all the rider information is recorded. The PNR information includes, for each rider, particulars of all flights in the path, the engagement category, and rider specific information such as frequent-flier rank, fining position, and the agent or channel through which the engagement originated. Each PNR is besides specified whether the rider was a no-show for the specified flight. In the simplest theoretical account the average no-show rate over a group of similar historical flights is computed. The mean in bend used to foretell the figure of no-shows over all engagement categories. The passenger-level theoretical account given by can be implemented utilizing any categorization method capable of bring forthing the normalized chances. The PNR records are partitioned into sections, and separate prognostic theoretical accounts are developed for each section. In the passenger-level mold we characterize each utilizing the PNR inside informations. Let Xi ; one = 1aˆÂ ¦aˆÂ ¦aˆÂ ¦aˆÂ ¦..I denote I characteristics associated with each rider. Uniting all characteristics yields the characteristic vector Ten = [ X1aˆÂ ¦aˆÂ ¦aˆÂ ¦Xi ] Each rider, n = 1aˆÂ ¦aˆÂ ¦aˆÂ ¦aˆÂ ¦aˆÂ ¦.N, booked on flight m is represented by the vector of characteristic values xmn = [ xmn, 1aˆÂ ¦aˆÂ ¦aˆÂ ¦aˆÂ ¦aˆÂ ¦aˆÂ ¦ xmn, iaˆÂ ¦aˆÂ ¦aˆÂ ¦aˆÂ ¦aˆÂ ¦.. xmn, I ] We know the predicted no-show rate from the historical theoretical account ; it is assumed the rider inherits the no-show rate. The rider degree prognostic theoretical account is so stated as follows: given a set of category labels cmn a set of characteristic vectors xmn and a cabin degree historical anticipation A µmhist predict the end product category of rider N on flight m: P ( C = cmn | A µmhist, X= xmn ) We are specifically interested in the no-show chance, cmn = NS, and compose this chance in the simplified signifier P ( NS | A µmhist, xmn ) The figure of no-shows in the cabin is estimated as a?‘ P ( NS | A µmhist, xmn ) The summing of chances for each rider in the cabin, gives no-show rate for the cabin. An correspondent attack can besides be used to foretell no-show rates at the fare-class degree. Lawernce, Hong, Cherrier4 comparison consequences computed utilizing the historical, passenger-level, and cabin-level theoretical accounts. The theoretical accounts were built utilizing about 880,000 PNRs booked on 10,931 flights, and evaluated against 374,900 PNRs booked on 4088 flights. The figure shows a conventional lift curve computed utilizing the three different executions of the passenger-level theoretical account. Figure 5: Addition Charts Beginning: Lawernce, Hong, Cherrier4 Each point on the lift curve shows the fraction of existent no-shows observed in a sample of PNRs selected in order of diminishing no-show chance. The diagonal line shows the baseline instance in which it is assumed that the chances are drawn from a random distribution. The three executions of the passenger-level theoretical account place about 52 % of the existent no-shows in the first 10 % of the sorted PNRs. This is one of the manner the Airlines can integrate informations excavation theoretical accounts integrating specific information on single riders can bring forth more accurate anticipations of no-show rates than conventional, historical based, statistical methods. Application of Data Mining technique to Strategies Customer Relationship Management In the current clip most of the industries utilizing frequence selling plans as a scheme for retaining client trueness in the signifier of points, stat mis, dollars, beans and so on. Airlines are a large fan of this Kingfishers Kingmiles, Jet Airways Jet Privilege, American Airlines AAdvantage, Japan Airlines Mileage Bank, KrisFlyer Miles etc. they all seemed to hold carved their ain individualities. Frequent Flyer Program presents an priceless chance to garner client information. It helps to understand the behavioral forms, unveil new chances, client acquisition and keeping chances. This helps Airlines to place the most valuable and the appropriate schemes to utilize in developing one-to-one relationships with these clients. The aim of informations mining application over the frequent circular client informations could be many, but ideally it is as follows: Customer cleavage Customer satisfaction analysis Customer activity analysis Customer keeping analysis Some of the illustrations in each class are: Classify the clients into groups based on sectors most often flown, category, period of twelvemonth, clip of the twenty-four hours, intent of the trip. Which types of clients are more valuable? Do most valuable clients receive the value for money? What are the properties and features of the most valuable client sections? What type of run is appropriate for best usage of resources? What are the chances to up-selling and cross-selling, for illustration hotel engagement, ascent to following category, recognition card, etc. Design bundles or grouping of services Customer acquisition. Yoon6 designed a database cognition find procedure dwelling of five stairss: choosing application sphere, mark informations choice, pre-processing informations, pull outing cognition, and reading and rating. This survey refers to the Yoon procedure to cover with three excavation stages, including the pre-process, data-mining, and reading stages for air hoses, as illustrated in figure below. Figure 6: database cognition find procedure Beginning: Yoon6 Some straightforward solution can be implemented that can besides be scaled-up in future like K-means, Kohonen self-organizing webs and categorization trees. In the instance of K-means algorithm, it is applied on client informations, delegating each to the closest bing bunch centre. The K- means theoretical account is run with different bunch figure until K-means bunchs are good separated. In the instance of categorization trees ( C5.0 ) , we derive a simple regulation set to unambiguously sort the complete database. Again, we have to bring forth the properties, ensuing from the sequence of flight sections. The truth of the prognosis for each section is provided by equilibrating the preparation set harmonizing to every bit sized bunchs. We regulate the figure of subsequent regulations, while finding a minimum Numberss of records given within each subgroup. Maalouf and Mansour7 did the survey based on 1,322,409 client activities minutess and 79,782 riders for a period of 6 old ages. They prepared Data based on Z-Score Normalization and ran the multiple questions and transformed the informations to make the bunch input records. They used K-means and O-Cluster algorithms. The consequence generated by constellating provides client cleavage with regard to of import dimensions of clients demands and value. The tabular array below is the consequence is a sum-up of the profile produced by k-means constellating that includes: gross milage, figure of services used, and client rank period. Figure 7: Clustering consequence on Airline Customer Data Beginning: Maalouf and Mansour7 The consequences generated by k-means constellating are used as a footing for the association regulations algorithm. Two different scenarios have been applied. The first scenario is based on Financial , Flight , and Hotel activities with 1,896 records. The 2nd scenario is based on the flight activities particularly the sectors, with 1,867 records. Figure 8: Association regulations for best client activities Beginning: Maalouf and Mansour7 Some of the take manner from Meatloaf and Mansour7 survey. Clustering utilizing k-means algorithm generated 9 different bunchs with specific profile for each one. From the bunch analysis it can be found which are the best client bunchs ( higher milage per rider ) than other bunchs. Necessitate a keeping scheme for these bunchs. Cross Selling schemes can be formulated between the bunchs ( for illustration between: 15 and 11 ; 13 and 17 because they are close in services value. The bunch analysis provides an chance for the air hose to bring forth more gross from a client. For illustration, the air hose could use an up-selling scheme by selling a higher menu place depending on the bunchs. From the bunch analysis Airline may follow an enhanced scheme for clients in bunchs in order to increase services usage and gross milage per rider. Plan for marketing run or particular offers by analysis through association regulations, for illustration, the clients utilizing the Flight and Financial services neer use the Hotel Services and the clients utilizing the Flight and Hotel services neer use the Financial Services. By analysing the services used in different bunchs, Airline can qualify services integrating. It enables the air hose to function a client the manner the client wants to be served. Application of Data Mining Application technique to understand the Impacts of Severe Weather Severe conditions has major impacts on the air traffic and flight holds. Appropriate proactive schemes for different severe-weather yearss may ensue in betterment of holds and cancellations. Therefore, understanding en-route conditions impacts on flight public presentation is an of import measure for bettering flight public presentation. Zohreh and Jianping8 in their survey proposed a model for informations mining attack to analysis of conditions impacts on Airspace system public presentation. This attack consists of three stages: informations readying, characteristic extraction, and informations excavation. The information readying stage includes the usual procedure of choice of informations beginnings, informations integrating, and informations data format. Figure 9: Model proposed by Zohreh and Jianping8 He used three informations beginnings: Airline Service Quality Performance ( ASQP ) , Enhanced Traffic Management System ( ETMS ) , and National Convective Weather Forecast ( NCWF ) supplied by National Center for Atmospheric Research. He used NCWF informations from April through September 2000 to stand for the terrible conditions season. These data-sets included the scheduled and existent going and arrival times of each flight of 10 coverage air hoses, tail figure, wheels off/on times, cab times, cancellation and recreation information, planned going and arrival times, existent going and arrival times, planned flight paths, existent flight paths, and cancellations, flight frequences between two airdromes, intended flight paths between two airdromes, flight holds, flight cancellations, and flight recreations. The image cleavage stage resulted in a set of severe-weather parts. Then for each of these parts, a set of conditions characteristics and a set of air traffic characteristics are extracted. A twenty-four hours is described by a set of severe-weather parts, each holding a figure of conditions and traffic characteristics. As a consequence of this survey it was found that there is strong correlativity of out of use flights, # of bad conditions parts, bad conditions airdromes, blocked distance, bad conditions longitude, by base on balls distance, bad conditions latitude, # of bad conditions pels with flight public presentation. Similarly the bunch algorithms ( like K-means ) can be applied. The outlook is that the same bunchs have similar conditions impacts on flight public presentation. Zohreh and Jianping8 generated bunchs for the full air space It was found that a bunch with worse conditions about ever had bad public presentation. The bunchs with big per centum of out of use flights, beltway distance, and blocked distance had a worse public presentation. These consequences were promising and showed that yearss in a bunch have similar conditions impacts on flight public presentation Other informations excavation attack which can be applied is Classifications. Application of Classification can assist us detect the patterns/rules that have important impact on the flight public presentation. Discovered regulations may be used to foretell if a twenty-four hours is a good or a bad public presentation twenty-four hours based on its conditions. For illustration Rule for Good: if % BlockedFlights lt ; = xxx and BypassDistance lt ; = yyy so Good ( n, prob ) There can be different ways where we can use informations mining attack to analysis of upwind impact on air hose public presentation. It seems to be that consequences obtained from constellating and categorizations were really meaningful for air hose and riders to be after in front. Application of Data Mining techniques to guarantee safety and security of Airlines rider The reaction of the terrorist onslaught on 26/9 and 11/9 end point in addition Security at airdromes: It ends up leting merely ticketed riders past the security Gatess, screen carry-on baggage more carefully for possible arms. The inquiry is whether these stairss could hold avoided the onslaughts, the people involved in the onslaught had legitimate tickets, and transporting box cutters and razor blades ( like in any other normal individual would make ) . The uncommon was the combination of their features, like none were U.S. citizens, all had lived in the U.S. for some period of clip, all had connexions to a peculiar foreign state, all had purchased one-way tickets at the gate with hard currency. With the sum of informations available about the rider during fining, the can be reviewed to qualify relevant available rider information. Give a rider s name, reference, and a contact phone figure, assorted informations bases ( public or private ) can place the societal security figure ( SSN ) , from which much information will be readily available ( recognition history, constabulary record, instruction, employment, age, gender, etc. ) . Since there is big figure of features available on both single riders, it will be of import to placing signals within the natural variableness or noise . If predicted incorrectly, this may take to either falsely confining an guiltless rider or neglecting to confine a plane that carries a terrorist. The air hoses already collect much informations on assorted flights. When the informations come in the signifier of multiple features on a individual point, exploratory tools for multivariate informations can be applied, such as categorization, arrested development trees, multivariate adaptative arrested development splines/trees. The security of the air transit can be improved well through modern, intelligent usage of pattern acknowledgment techniques applied to big linked databases. Similarly Data excavation techniques can be used for the Safety of the rider. An air safety office plays a cardinal function in guaranting that an air power organisation operates in a safe mode. Currently, Aviation Safety offices collect and analyze the incident studies by a combination of manual and machine-controlled methods.. Data analysis is done by safety officers who are really familiar with the sphere. With Data mining one can happen interesting and utile information hidden in the informations that might non be found by merely tracking and questioning the information, or even by utilizing more sophisticated question and coverage tools. In a survey done by Zohreh Nazeri, Eric Bloedorn, Paul Ostwald10 it was found that happening associations and distribution forms in the informations, conveying of import interior. The other determination is Associating the incident studies to other beginnings of safety related informations, such as aircraft care and conditions informations, could assist happening better causal relationships. SumMRry Business Intelligence through efficient and appropriate Data excavation application can be really utile in the Airline industry. The Appropriate action programs from the information excavation analysis can ensue in improved client service, aid bring forthing considerable fiscal lift and set the hereafter scheme.

How to Write a Zoology Term Paper Extended Guide

How to Write a Zoology Term Paper Extended Guide If you’re taking the course of Zoology, you’ll be there to agree that writing a term paper for this discipline is the toughest part. The point is that Zoology is a huge and serious subject and college students need to be too careful and attentive when they choose the topic for the paper. The problems here is that students usually do not get the idea of how huge the project is, which is why later they experience troubles and fail to submit their assignments within the set deadline. Without a doubt, Zoology is a huge area to write an academic paper on. For that reason, you have to pick the field of study first to produce a term paper on. This zoology term paper writing guide aims to explain you all the aspects of producing a top-notch Zoology term paper. Choose the Topic for Your Zoology Term Paper: A Few Lifehacks from Our Writers Commonly, the Zoology term paper comprises maximum 3500 words or from 8 to 10 pages. In order to craft a high-quality Zoology project, you have to follow some crucial steps. Picking the most suitable topic is the first one to take. If you’re struggling to come up with a good idea for your Zoology term paper writing, here are some themes you are free to use, or at least find some inspiration from: Then here are 10 you can use, or at the very least get some inspiration from when writing your zoology term papers: Ecology Evolutionary Zoology. This is the area that specializes in studying how animals interact with the environment. You, as the author of the term paper, need to concentrate on how animals shape the environment, as well as use it to their advantages. Epidemiology Disease Transmission. If you’re interested in diseases and how to treat them, this topic could be the option. Use your Zoology term paper to see how diseases affect the population sizes of different species, as well as how this or that diseases has led to the decline of various species. Urban Wildlife. It is not a secret that animals are being forced to live in an urban environment at the present moment. Many of them can adapt while the other have to somehow fight for their life. A term paper dedicated to how animals tend to survive in the modern world would be a thing of importance to every reader of yours. Early Life History Larval Ecology of Amphidromous Fish. If you’re particularly interested in the topics related to the freshwater ecology, check out the research options in the area of biology and ecology of freshwater fish. Animal Behavior Neurobiology. As the name suggests, this topic is a study of how animals behave, as well as why they act this or that way. There’s much space left for research within the area, which means you won’t experience any problems when conducting the research. Cellular Mechanism. How the organisms of animals function at the cellular level is just as interesting as it is when it comes to human beings. Since this subject area has a bunch of links to many various branches, it is very diverse and easy to research. Unusual Behaviors. At times, the ways the animals behave seem to be pretty counterintuitive. A complete project written on the strange behaviors of animals would make sense and be interesting to read no matter how bizarre. Migration Patterns. Provide a Zoology term paper on how animals tend to migrate, as well as why they do that to the chosen areas and which areas they settle in. The Zoo Life. A Zoology term paper on the life of animals at the zoo is something your target audience can relate to since this is where we all go when we’d like to see a lynx or an elephant in the flesh. Exotic Animals. Why not write a Zoology term paper on the exotic animals? Use your college project to provide information on more exotic animals than the average individual in the street can even imagine. There are many animals out there that you can mention in your project to amaze your tutor, who is not even aware of their existence. Hunting Of Animals. This topic provides you with an opportunity to do an in-depth research on how hunting impacts the population numbers, as well as the mortals behind hunting some species. Continue with Drafts Once you decide on which topic you’d like to work on, the process of real writing starts. It is recommended to create your outline in a separate file in order to be able to expand on every other point, adding information and correcting the details. Are you done with the first draft? Don’t even try to edit it! Don’t stop to pick a better word here or there to boost the sentence structure. You will have time to do it later! Instead, let your ideas and thoughts flow to compose a complete assignment. The Title Page There are different ways of crafting a title page for a Zoology term paper just like bibliographies such as APA or MLA. If you’re required to provide the title page of MLA format, remember that it needs double-spacing and that all letters should be centered. Then, provide the name of your college or university. Skip to about one-third of the title page and put down your term paper title, provide a subtitle if you have one. Now make sure to skip several lines down and write your name, the course number, and name, your tutor’s name and the due date of your Zoology project. As for APA style, ensure to avoid contractions and abbreviations in your title that shouldn’t be more than 12 words in lengths. What is more, the author should also keep away from using words that serve no purpose. In other words, the title of the Zoology term paper of APA format should be concise and clearly inform your reader on what your project is about. Make sure to write the title beginning every word with a capital letter and don’t forget to center-align in. Then provide your name, the academic course that you take, the name of your tutor and the submission date. Table of Contents The table of contents is an organized listing of the key sections and headings, as well as sub-sections and sub-headings of the document. Thus, your readers will immediately see how your paper is organized and then skip down to the term paper sections that are the most interesting to them. It is important to mention that a concise and clear table of contents is the first proof your term paper is a good one. Checklist for the table of contents: Properly formatted; Highlights the key sections of the term paper starting with the Dedications page. If you do not provide the Dedicate page, the Abstract page is the starting point; All headings and titles match exactly what is provided within the paper content; List the titles of every chapter together (do not provide any subsections!); All page numbers must be correct. Introduction In the introduction of the Zoology academic project, you are supposed to make your readers familiar with the paper topic and create interest in reading your piece further. The introductive section precisely describes the main purpose of your research and informs your target audience on why you’ve conducted one. Make sure to briefly review the previous research on the issue with enough background info in order to guide your reader (this can be done by a literature search of peer-viewed, published and primary materials). It is highly important to properly reference the background information. Imagine your introductive section as a sort of a funnel. Begin with stating a very broad topic, field of study, problem and so on. If your term paper is dedicated to the life of aged animals in zoos, feel free to start your introduction by providing the fact that â€Å"Multiple improvements in nutrition and veterinary care of animals that live in zoos have led to an increase in the longevity of these animals over the past 30 years.† From this broad intro, you may switch to a more specific research issue. The final part of the introductive section should also include a statement of the hypothesis you’ve studied and your predictions. When it comes to the hypothesis, the question is about a general statement of casualty for a Zoology observation or pattern. It is recommended to start this paragraph with â€Å"It was hypothesized that A affects B†¦Ã¢â‚¬ , â€Å"We hypothesized that A affects B†¦Ã¢â‚¬ , etc. The hypothesis usually provides a general â€Å"effect† and some specific prediction statements. Provide the specific questions that you’re going to answer, a short introduction of the general method used, as well as how your project will help expand the knowledge in a particular area. Main Body The body of the Zoology term paper is the longest part. It comprises sections and sub-sections. You, as the term paper author, must state a main point or argument in every section and support it with appropriate information. Each argument should be developed in an ineligible way. If you need to quote some text from journals or books, make use of the so-called in-text citation. For every in-text citation, there must be provided the corresponding entry in the reference list. Example paragraph with an in-text citation: ‘Zoos are required to maintain a high standard of animal welfare, and this can be assessed using a combination of resource-based and animal-based indices usually divided into behavioral indicators, physiological indicators and clinical/pathological signs (Wolfensohn, Shotton, Bowley, Davies and Thompson, 2018). References: Wolfensohn, S.; Shotton, J.; Bowley, H.; Davies, S.; Thompson, S.; Justice, W.S.M. Assessment of Welfare in Zoo Animals: Towards Optimum Quality of Life. Animals 2018, 8, 110.’ If you deal with the online information sources, it is important to keep in mind that cyber sources may not be cited (with a couple of exceptions!) unless they’re internet peer-reviewed materials. In case a college student has an item (an online government publication for example), it is recommended to approach academic tutor to know more details about the specific requirements for the citation part. However, if this or that article can be found only on the web, the author of the Zoology term paper must cite the reference as provided below: ‘Higginbotham, S., W.R. Wong, R.G. Linington, C. Spadafora, L. Iturrado, and A.E. Arnold. 2014. Sloth hair as a novel source of fungi with potent anti-parasitic, anti-cancer and anti-bacterial bioactivity. PLoS ONE 9: e84549. doi:10.1371/journal.pone.0084549.’ Conclusion Use the conclusive section of your Zoology term paper to state the problem that you have posted, as well as provide an explanation of the results that you have revealed during the research. Produce a brief summary of your observations and interpretations, as well as write a brief description of the results (don’t go into too much details about this!). For instance: ‘The oscillation in the abundance of different groups of prey at different periods maintained the overall prey abundance at a relatively constant level throughout the year, mitigating the effects of prey availability on the abundance and reproductive period of the scorpions. These results suggest that microhabitat exploitation is a key factor to sustain litter-dwelling scorpions in disturbed forest remnants and that T. pusillus can be an ecological indicator of edge effects. (WeltonDionisio-da-SilvaAndrà © Felipe de AraujoLiraCleide Maria Ribeiro deAlbuquerque, Zoology. Volume 129, August 2018, Pages 17-24)’ Besides, you should also highlight the limitations and strengths of your research. What is more, you have an opportunity to provide any suggestions for future work in a particular field of study. References / Bibliography In the bibliography section of your Zoology term paper, you’re required to provide each information source in the proper citation style. Appendices Appendices are an integral part of an academic term or research paper. Usually, they are comprised of the complete list of info of the maps, survey forms, charts, figures, stats, graphs and so on that the author used in the project. Mind that these elements are not included in the actual word count of the project.