Sagar Yeruva

Machine Learning offers significant potential facilities to farmers in improvising the productivity of agriculture. This paper briefs detailed analysis of several such research proposals that have been published in the domain of agriculture and thus presents a detailed solution as well. The cutting-edge technologies like Internet of Things, Artificial Intelligence and Big Data Analytics are used in most of the research proposals to give various solutions to the existing agricultural problems. This paper will help in addressing significant gaps found in those studies and listed out the problems in agriculture that are to be solved. To exemplify some gaps, the studies were limited to specific parameters of soil, without considering the relations and functionalities between soil parameters, micro & macronutrients. The main objective of this study is to find the correlation between the soil parameters that affect the yield of crop by generating rules. This exploratory study has been done by conducting the field activity and generated appropriate rules.

Evolving technologies make human life easier with increasing challenges. Online payments have become an integral part of our lives in the era of digitalization. The credit card payment system has made transactions hassle-free. This led to E-Commerce appraisal. Digitalization of transactions has given rise to new forms of fraud and cyberattacks that can affect individuals and organizations. This had set hackers at a great deal to steal the cardholder details using different schemes. Credit card companies must recognize these fraudulent transactions at the earliest to retain credibility among the stakeholders. Traditional methods of fraud detection have proven ineffective in identifying and preventing these fraudulent activities and cyberattacks in real time. This paper discusses various Machine Learning algorithms that predict fraudulent transactions in real-time. Fraudulent activities are solved using data science and machine learning techniques with substantial processing power and the capacity to manage massive datasets. The model is trained on large volumes of the dataset. This paper emphasizes comparison of various machine learning algorithms' performance over the input. The accuracy and efficiency of several machine learning algorithms are measured and analyzed through tabulation and comparison. The trained model is integrated with a website to categorize financial transactions as either legitimate or fraudulent. On utilizing advanced machine learning algorithms, credit card fraud detection systems have become more refined and accurate in recent years. As a result, financial organizations and customers are protected against such fraudulent activities, leading to increased trust and confidence in utilization credit card payments.

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine’s disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine’s health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, SVM, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine’s health.

Generally, only feature values obtained from photos are used to identify fish species. But, it is challenging to identify fish species based on an image alone because fish of the same species can have varying hues or seem quite similar to other species. Additionally, it can be a tedious task that might lead to wrong predictions. Since various fish species exist, it is difficult to determine a fish without a proper model. Fast-growing computing and sensing technologies have improved most embedded systems, which help us solve more complicated algorithms. The main challenge is to perceive and analyze corresponding information for better judgment. An advanced system with better computing power can facilitate identifying fish species. Using the Teachable machine, a web-based tool for creating machine learning models, we can ensure that this application gives accurate results in classifying various fish species. An application that uses machine learning to identify fish categories is developed in this study by capturing images of fish and identifying their categories. In addition to providing fish information, this app also connects users with other fishermen, gives feedback on the fish, display catch logs, supports multilingual display of data, fish focused advisory chatbot, and market value information. User dashboards allow users to sign up, create profiles, scan, and identify their catches. This mobile application ensures the data integrity and confidentiality of the user’s data. The overall performance of the application is responsive and user friendly.

Nowadays, people are constantly moving towards various fashion products as a result the e-commerce market for garments is growing rapidly. Online stores must update their features according to user requirements and preferences. However, there are too many options for users to select from these online stores which may leave them in a dilemma to identify the correct outfit, save the user time, and increase sales, efficient recommendation systems are becoming a necessity for online retailers. In this paper, we proposed an Apparel Recommendation System that generates recommendations for users based on their input. We used a real-world data set taken from the online market giant Amazon using Amazon’s Product Advertising API. We aim to use keywords like brand, color, size, etc., to recommend. Data exploration to get detailed information about our dataset, Data Cleaning(pre-processing) to remove invalid sections, Model selection (We have compared different feature extraction techniques like bag of words, TF-IDF, and word2vec model) to find out efficient techniques and Deployment of the model that could facilitate recommendation system to simplify the task of apparel recommendation system. The accuracy of the model is identified using the response time and content matching.

A molecule called hemoglobin is found in red blood cells that holds oxygen all over the body. Hemoglobin is elastic, round, and stable in a healthy human. This makes it possible to float across red blood cells. But the composition of hemoglobin is unhealthy if you have sickle cell disease. It refers to compact and bent red blood cells. The odd cells obstruct the flow of blood. It is dangerous and can result in severe discomfort, organ damage, heart strokes, and other symptoms. The human life expectancy can be shortened as well. The early identification of sickle calls will help people recognize signs that can assist antibiotics, supplements, blood transfusion, pain-relieving medications, and treatments etc. The manual assessment, diagnosis, and cell count are time consuming process and may result in misclassification and count since millions of red blood cells are in one spell. When utilizing data mining techniques such as the multilayer perceptron classifier algorithm, sickle cells can be effectively detected with high precision in the human body. The proposed approach tackles the limitations of manual research by implementing a powerful and efficient MLP (Multi-Layer Perceptron) classification algorithm that distinguishes Sickle Cell Anemia (SCA) into three classes: Normal (N), Sickle Cells(S) and Thalassemia (T) in red blood cells. This paper also presents the precision degree of the MLP classifier algorithm with other popular mining and machine learning algorithms on the dataset obtained from the Thalassemia and Sickle Cell Society (TSCS) located in Rajendra Nagar, Hyderabad, Telangana, India. Doi: 10.28991/esj-2021-01270 Full Text: PDF