Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements

INTRODUCTION: The credit card deception poses a global threat, resulting in significant monetary losses and identity theft. Detecting fraudulent transactions promptly is crucial for mitigating these losses. Machine learning algorithms, specifically the random forest algorithm, show promise in addressing this issue. OBJECTIVES: This research paper presents a comprehensive study of numerous machine learning algorithms for credit card deception recognition, focusing on the random forest algorithm. METHODS: To tackle the increasing fraud challenges and the need for more effective detection systems, we develop an advanced credit card deception detection system utilizing machine learning algorithms. We evaluate our system's performance using precision, recall, & F1-score metrics. Additionally, we provide various insights into the key features for fraud detection, empowering financial institutions to enhance their detection systems. The paper follows a structured approach. RESULTS: We review existing work on credit card fraud detection, detail the dataset and pre-processing steps, present the random forest algorithm and its application to fraud detection, compare its performance against other algorithms, discuss fraud detection challenges, and propose effective solutions. CONCLUSION: Finally, we conclude the research paper and suggest potential areas for future research. Our experiments demonstrate that the random forest algorithm surpasses other machine learning algorithms in accuracy, precision, recall, & F1-score. Moreover, the system effectively addresses challenges like imbalanced data and high-dimensional feature spaces. Our findings offer valuable insights into the most relevant features for fraud detection empowering financial organizations to improve their fraud detection capabilities.

Section: F1 Scorementioning

confidence: 99%

Credit Card Deception Recognition Using Random Forest Machine Learning Algorithm

Jaiswal,

Bharadwaj,

Kumari

et al. 2024

“…To do this, the training prediction fault is propagated back through the net, and the heaviness is adjusted based on the error gradient [22]. Finding the ideal set of weights for a neural network, which might include millions of nodes, is a computationally intensive operation often run on a graphics processing unit (GPU).…”

Section: Ff-nnmentioning

confidence: 99%

“…A gang might alter transactions by mining "invalid" blocks or double-spending if it were to acquire this much power. Because most miners use ASICS mining rigs, they can only operate through pools [22,23]. The energy in some collections is so great that it might get abused.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of operational conditions of fuel cells by using machine learning

Shrote,

Kumar,

Kaur

et al. 2024

The reliability of fuel cells during testing is crucial for their development on test benches. For the development of fuel cells on test benches, it is essential to maintain their dependability during testing. It is only possible for the alarm module of the control software to identify the most serious failures because of the large operating parameter range of a fuel cell. This study presents a novel approach to monitoring fuel cell stacks during testing that relies on machine learning to ensure precise outcomes. The use of machine learning to track fuel cell operating variables can achieve improvements in performance, economy, and reliability. ML enables intelligent decision-making for efficient fuel cell operation in varied and dynamic environments through the power of data analytics and pattern recognition. Evaluating the performance of fuel cells is the first and most important step in establishing their reliability and durability. This introduces methods that track the fuel cell's performance using digital twins and clustering-based approaches to monitor the test bench's operating circumstances. The only way to detect the rate of accelerated degradation in the test scenarios is by using the digital twin LSTM-NN model that is used to evaluate fuel cell performance. The proposed methods demonstrate their ability to detect discrepancies that the state-of-the-art test bench monitoring system overlooked, using real-world test data. An automated monitoring method can be used at a testing facility to accurately track the operation of fuel cells.

“…Khasim, Rahat, Ghosh [27], and colleagues' 2023 article, "Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh", explores AI-based solutions for diagnosing rice-leaf diseases. Deciphering Microorganisms through Intelligent Image Recognition", authored by Khasim, Ghosh, Rahat [28] and others in 2023, discusses the use of machine learning and deep learning in identifying microorganisms through advanced image recognition techniques. The 2023 study by Mohanty, Ghosh, Rahat, and Reddy [29], "Advanced Deep Learning Models for Corn Leaf Disease Classification", focuses on the application of deep learning in classifying diseases in corn leaves based on a field study.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Stroke Predictor Application

Kishore Kanna,

Rami Reddy,

Panigrahi

et al. 2024

When blood flow to the brain stops or slows down, brain cells die because they don't get enough oxygen and nutrients. This condition is known as an ischemic stroke. It is now the biggest cause of death in the whole planet. Examining the afflicted people has shown a number of risk variables that are thought to be connected to the stroke's origin. Numerous studies have been conducted to predict the illnesses associated with stroke using these risk variables. The prompt identification of various warning symptoms associated with stroke has the potential to mitigate the severity of the stroke. The utilization of machine learning techniques yields prompt and precise predictive outcomes. Although its uses in healthcare are expanding, certain research domains have a stronger need for more study. We think that machine learning algorithms may aid in a deeper comprehension of illnesses and make an excellent healthcare partner. The textual dataset of numerous patients, which includes many medical variables, is gathered for this study. The missing values in the dataset are located and dealt with during processing. The dataset is used to train machine learning algorithms including Random Forest, Decision Tree classifier, and SVM. The method that delivers the greatest accuracy for our dataset is then selected once the accuracy of the algorithms has been determined. This aids patients in determining the likelihood of a brain stroke and ensuring they get the right medical attention.