Execution of Market Basket Analysis and Recommendation Systems in Physical Retail Stores to Advance Sales Revenues

In the early months of 2020, pandemic covid-19 hit many parts of the world. Especially developing countries like India observed a negative growth rate in few quarters of last financial year. Retailing is one of the key sectors that contribute to Indian GDP with a share of nearly 10 percent. Hence there is a need for the retail sector to bounce back which is possible with the efficient use of new digital technologies. Market basket analysis is used here to extract the association rules which can be directly used for formulating discount and combo offers. Along with that, these rules can be used to decide the product positioning in the retail store. Items which are bought together can be placed next to each other to increase sales. Recommendation systems are most commonly used in ecommerce websites like Amazon, Flipkart, etc, and streaming platforms like Netflix to recommend the items that are to be purchased by users. Although recommendation engines are implemented in multiple web and mobile applications, these are not in the implementation stage in offline retail stores due to many implications associated with them like infrastructure, cost, etc. In this project, we have used market basket analysis and recommendation systems to propose a model to implement in retail stores to increase sales revenues and enhance customer experience. © 2022 IEEE.

Execution of market basket analysis and recommendation systems in physical retail stores to advance sales revenues

Pandemic COVID-19 struck numerous regions of the planet in the first few months of 2020. India and other emerging nations in particular saw negative growth over a few quarters of the previous fiscal year. With a contribution of over 10%, retailing is one of the major industries that contribute to India's GDP. As a result, the retail industry must recover, which may be done with the effective application of new digital technology. Here, association rules that may be utilised to create discounts and package deals are extracted using market basket analysis. Additionally, similar guidelines may be applied to determine where to arrange a product in a retail setting. Items purchased in bulk can be arranged adjacent to one another to improve sales. To suggest the products that consumers should buy, recommendation algorithms are most frequently employed in e-commerce websites like Amazon, Flipkart, etc. and streaming platforms like Netflix. Although there are numerous online and mobile apps that use recommendation engines, physical retail businesses have not yet adopted them owing to the various consequences they have, such as infrastructure, cost, etc. In this project, we've used market basket research and recommendation algorithms to develop a model that can be used in retail establishments to boost sales and improve customer satisfaction. © 2022 IEEE.

Arduino based Anti-Face-Touching Device and prediction model

Due of the fast pace at which COVID-19 may spread through respiratory illness, the terrible condition it was in heightened public tension. The WHO's primary recommendations advised against often touching your face in order to avoid the transmission of viruses through your lips, eyes, and nose. According to research, the typical person was discovered to touch their face about 20 times each hour since it is everyone's unconscious behavior. In order to cope with this, the study suggests a hardware model that recognizes hand motions that are made in the direction of the user's face and alerts them to such movements using both aural and visual sensory feedback modalities. In order to create a model for the prediction of facial touch motions, the study analyses deep learning architectures in more detail. The FaceGuard device, which is a deep learning-based prediction model used to determine whether or not a hand movement would result in face contact, is compared to the accuracy of the suggested hardware model in the paper 'FaceGuard: A Wearable System To Avoid Face Touching1.' It alerts the user through vibrotactile, aural, and visual sensory modalities. After investigation, it was discovered that the hardware model had less accuracy than the deep learning model and required shorter time to respond to vibro tactile sensory data. © 2022 IEEE.

Online based Viva Voce Control System

Viva voce is an assessment method carried out by academic staff to assess the knowledge capacity of candidates. The assessment is usually held physically. With the Covid-19 pandemic, the whole process has been shifted onto an online context. There are several difficulties that come across when conducting online, such as marking of answers, guaranteeing the honesty of the candidate, and the manageability of the whole viva session. This research paper discusses the solution to the problem of conducting and managing online viva voce assessments. The proposed solution consists of mechanisms such as, a sandboxed environment to isolate the application, an advanced authenticating system to identify the intended candidate, a comprehensive monitoring system to monitor the candidate during the assessment, an answer validating system to provide a percentage mark to the answers provided by the candidate against a set marking scheme and finally a process to coordinate the viva voce session. © 2022 IEEE.

Smart Online Oxygen Supply Management though Internet of Things (IoT)

We are surrounded by oxygen in the air we We cannot even exist without the ability to breathe. The need for oxygen has increased during the COVID19 pandemic, and although there is enough oxygen in our country, the main issue is getting it to hospitals or those in need on time. This is simply due to a significant communication gap between suppliers and hospitals, so we plan to implement an idea that will close this gap using real-time tracking as we can track the movement of oxygen tankers by gathering the requirements. We are using an ESP32 Wi-Fi module, a MEMS pressure sensor that enables the combination of precise sensors, potential processing, and wireless communication, such as Wi-Fi, Bluetooth, IFTTT, and MQTT protocols, to implement it successfully. The pressure sensor publishes the value of oxygen remaining from the location to the MQTT broker. © 2022 IEEE.

Develop a Model for Assessing the Most Efficient Diseases Diagnosis using Machine Learning

so, machine learning techniques are being developed to improve performance and maintenance prediction. Increasing our knowledge of the relationship between humans and algorithms, Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. Numerous researchers recently developed numerous computer-aided diagnostic algorithms employing various supervised learning approaches. Early identification of sickness may help to reduce the number of people who die as a result of these illnesses. Using machine learning techniques, this research creates an efficient automated illness diagnostic algorithm. We chose three key disorders in this paper: coronavirus, cardiovascular diseases, and diabetes. The data are inputted into a mobile application in the suggested model, the investigation is then done in a real-time dataset that used a pre-trained model machine learning technique trained within the same dataset then implemented in firebase, and lastly, the illness identification result can be seen in the mobile application. Logistic regression is a method of prediction calculation © 2022 IEEE.

Predicting the cumulative number of cases of the COVID-19 epidemic in india from early data

Aim: The present study aims at explaining the epidemic situation in India for COVID-19 and forecasting the expected rise in the positive cases in India. Objective: This study will be useful for Government authorities and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. This research would also be useful in predicting outbreak numbers with greater precision for people involved in exploring this deadly disease. Methods: We used the Support Vector Machine (SVM) to forecast and analyze the COVID-19 situations to predict future trends. On definite trail and model training, it was observed that the number of COVID cases will increase for the next four days. Results: The SVM model predicted accurate results. The prediction accuracy seems to best fit and indicates the cases to rise in the next coming days. Confirmed cases and the SVM predictions are close to each other, thus proving the accuracy of the SVM predictions. It was inferred that the numbers of COVID-19 instances will rise if the same trend is followed. Conclusion: The COVID-19 outbreak is exacerbated by secondary hospital transmission. Testing, particularly of those coming in with respiratory symptoms, is essential to isolate those in hospitals. A two-stage, pre-emptive testing is recommended in symptomatic older people immediately to reduce mortality. Immediate and on-going serological surveys are required to track the epidemic level. We are flying blind at the moment. The demand for the ventilators would be 1 million. The current supply in India is projected to range from 30 K to 50 K (the US has 160 K and is still running short). Health staff involved in treating COVID-19 patients also have to shield themselves using personal protection (i.e., masks and gowns) to save themselves from being infected. Thus, SVM model predictions will give a better insight into the growth of COVID-19 cases and, therefore, will allow the government of India to take adequate measures to restrain the issue at the earliest. © 2021 Bentham Science Publishers.