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Review Paper

Operations Research in tourism: A systematic literature review

The tourism sector is extremely competitive and characterized by fierce competition for the discretionary spending of tourists. To effectively compete in the tourism sector, a nation or region must be able to make informed strategic and operational decisions. In this decision-making process, the capacity to predict tourism demand with accuracy in the context of an ever-changing environment may be very helpful. Academic study on tourism is increasingly becoming the main topic as its economic significance increases. Research in this field aids in comparing best practices and spotting new patterns in tourist demand and supply. This is where the exploding use of Big Data in the recent year comes in. Big Data, a relatively newer phenomenon is a complex and huge data set that is derived from a combination of numerous individual data. The main objective of the research is to understand and review the existing research on the application of operation research and big data in the tourism sector for revenue and efficiency maximization, extrapolating data to estimate potential future demand, challenges faced by the hospitality sector, and comprehend the latest trends in tourism marketing, among other things. The paper evaluates the available studies on the tourist business sector using various software available.

Published by: Miit Virani, Mohak Saboo, Mudit Bagla, Nandini Sarin, Neeharika Khandelwal

Author: Miit Virani

Paper ID: V8I5-1157

Paper Status: published

Published: September 20, 2022

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Review Paper

Operations Research on service scheduling in the healthcare sector: A systematic review

The current state of healthcare in the world is dire, with millions of people unable to access healthcare services on time. One of the key reasons behind this problem is the lack of operational efficiency in many clinics and hospitals, caused due to unscientific scheduling practices. Hence, this paper seeks to conduct a systematic review of the existing research done in the field of healthcare service scheduling, and its multiple sub-sections viz. appointment scheduling, nurse rostering, home healthcare scheduling, etc. The bibliometric review found that majority of the research in the aforementioned field is scattered and lacks integration. Moreover, past research has primarily employed matheuristics, mixed integer linear programming and simulation approaches to solve service scheduling problems, without exploring other methods to a great extent. In the end, we suggest some topics in which future research can be conducted.

Published by: Aryan Smith, Aryan Jain, Anushka Agarwal, Arhan Shroff, Aryan Verma, Veerendra Anchan

Author: Aryan Smith

Paper ID: V8I5-1151

Paper Status: published

Published: September 19, 2022

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Research Paper

Emerging smart fashion and its scope in the future

Smart Fashion has recently attracted the attention of the world and people are enthusiastically adopting it. It is a new form of fashion with technology embedded in it, which makes our everyday activities more efficient and less time-consuming. This study provides information about how smart fashion is developing and the different sectors where it is being actively adopted by professionals and governments. The paper discusses the emergence and continual growth of smart fashion in various domains: military, healthcare, sports and marine. Smart fashion for the elderly smart textiles and gadgets for the youth have also been included in the study. Smart Fashion has yet not been extensively adopted by the world yet, however, the research work shows the enormous potential it carries; it throws light on how with the advancement of technology, it will become more and more aesthetically pleasing and comfortable for commoners and industrials.

Published by: Gaurika Vaswani

Author: Gaurika Vaswani

Paper ID: V8I5-1148

Paper Status: published

Published: September 16, 2022

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Review Paper

Carbon credits: Are they effective?

In today’s sustainable and environmentally responsible world, it is necessary to track and regulate a country’s CO2 emissions. This paper will evaluate if carbon credits in the form of carbon taxes have an effect on CO2 emissions and if there is, to what extent. The data used are from countries that have successfully established carbon tax. The paper will investigate the above by using a Linear Regression Model with the carbon tax, GDP per capita, and Area of the country as the explanatory variables and CO2 emission as a dependent variable.

Published by: Sanjana Saigal

Author: Sanjana Saigal

Paper ID: V8I5-1142

Paper Status: published

Published: September 16, 2022

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Research Paper

Nine-layer CNN for detection of the cancerous growth of abnormal cells in the brain

A brain tumor is a group development of abnormalities brain cells. There are numerous forms of brain tumors. Some brain tumors are cancerous, whereas others are noncancerous. Brain tumor identification involves several phases, including the capture of an input MRI image, the conversion of the input image to a grayscale image, the application of filters, segmentation, feature extraction, and classification. The detection of a tumor is a difficult process. The position, size, and shape of the tumor differ greatly from patient to patient, making segmentation a difficult process. The detection of a tumour is a difficult process. The position, shape, and structure of the tumour vary significantly from patient to patient, making segmentation a difficult process. A Nine Layer CNN architecture including an input layer, zero padding, Conv2D, Batch Normalization, Re-Lu, Max pooling, Max pooling, Flatten, and Dense layer is designed in this study. TCIA Brain tumor dataset is used to train the Nine Layer CNN. TCIA dataset is augmented to overcome overfitting circumstances. In order to overcome overfitting conditions, the TCIA dataset is augmented. CNN nine layer produced a decent outcome, with a training accuracy of 98.93%. If the classifier determines that the picture is a tumor present image, it will also provide the proportion of the tumor.

Published by: J. Guna Keerthana, N. Britto Martin Paul, S. Sravan Kumar, B. Kavya Pranathi, V. Divya, Prudhvi Kanth Bezawada

Author: J. Guna Keerthana

Paper ID: V8I4-1196

Paper Status: published

Published: September 14, 2022

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Research Paper

Credit Card Fraud Detection and Classification by Optimize Features and Deep Learning

Traditionally, rule-based systems have been the primary instrument for detecting fraud in today's financial systems, with fraud specialists defining the rules based on prior instances and outcomes. If a new transaction meets one or more of the previously established criteria, an alert is triggered, indicating that the new transaction may be fraudulent. For previously known fraud tendencies, the rule-based method is effective. Before adding a new rule to the current rule set, a sufficient number of fraudulent transactions must have happened that fit the rule. During this time span, fraud techniques may evolve, resulting in the induced rule expiring. Thus, the emphasis should be on using prior transactions that follow a rule-based approach in conjunction with an unsupervised method that detects previously unknown fraud activity. There is a need to use fraud detection systems that are capable of keeping up with the cardholder's updated spending behaviour. The detection process's goal is to identify as much fraud as possible while reducing the false positive rate, which has a negative effect on cardholder satisfaction as the cost of providing more false alarms increases. To accomplish this approach, the threshold value is determined at the account level of the cardholder by evaluating the probability sequence of previous and new incoming transactions. Additionally, identified fraudulent transactions are labelled in the database for future analysis in the event that additional assessment is required.

Published by: Razia Seema, Kumari Archana

Author: Razia Seema

Paper ID: V8I5-1146

Paper Status: published

Published: September 14, 2022

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