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Inflammation is known to be the root cause of the development of coronary artery disease (CAD). Subclinical inflammation is one of the key players in pathophysiology of CAD. An inflammatory response leads to the secretion of various adipokines and cytokines. There are various pro-inflammatory markers which could be used to determine the onset of disease, and one such marker is resistin adipokine. The present study evaluates and compares the circulatory concentration of resistin in CAD patients and controls for the determination of its association with CAD. In this study, 58 CAD patients were enrolled and were further subdivided into four groups as per the associated co-morbidities. Various anthropometric and biochemical parameters were assessed. The circulatory levels of resistin were found to be significantly elevated in CAD patients and their subgroups as compared to controls. We conclude that elevated resistin levels in CAD patients suggest a key role of resistin in the development of CAD. The present study also observed that co-morbidities like type 2 diabetes can act as one of the major factors for the elevated concentration of resistin in the circulation of CAD patients with diabetes.

Planning of crops for the next season has been a tedious task for the farmers as it is a difficult prediction about metrics of prices that their crop will fetch in a particular season which will be typically based on dynamic weather conditions. This leads to inaccurate prediction of crops’’ prices by farmers, and they happen to wrongly select the crops or in haste they happen to sell their crops early without storing and thus earning less than what the same crop would have fetched them in the future. This problem could be addressed by an ML model which will predict the prices of crops in advance showing the proper analysis of the crop and presenting their future scenario so that farmers can select the right crops to strategize crop production which involves crop selection, time of sowing deciding crop pattern and storage of harvested crops providing enough insights for predicting the appropriate price in the markets.

The usage of online payment systems is increasing multifold these days due to their transaction reliability and ease of completion in real-time. The most preferred service is UPI service, which is majorly available in Metropolitan cities, in some semi-urban and rural areas which have a proper communication infrastructure. However, there is a major issue to expand these services in rural areas, which lack proper communication infrastructure. Even after the internet revolution, it is ensured that all Indians are connected to data at all times but there might be situations where the user could not able to connect to the Internet owing to many reasons like High Altitude, Low Network availability, in-flight, etc. As an individual, such situations cannot be avoided, and there it comes to the applicability of offline transaction systems. We plan to change that by introducing an offline transaction system. We plan to expand on the idea of an offline payment system that will ensure that a reliable & secured digital payment system is available to all individuals.

One of the largest growing industries in the modern-day world is the sporting industry. Currently valued at around 500 billion USD, with a growth scope of exponential potential, its ability to attract investors is incredible. And just like any other investment. It is part andparcel of the investor’s fiscal responsibility to take good care of their assets. The biggest assets in the sporting industry are of course the players, and the greatest threat to said assets is injuries. We take into consideration said factors and deem it important to solve said issues, and understanding the money involved, the industry sides with us too. We seek to solve the said problemby taking into account all previous injury records and datasets of various players and predicting the kind, number, and severity of the injuries in the future. We seek to create a methodology for such prediction, which applies to all and any sports, being one of the only such models of its kind.

In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification.