Improving Fisheries Management through Deep learning based Automated fish counting

In today's digital age, detecting and preventing fraudulent credit card transactions is of paramount importance. As technology advances, criminal methods are also becoming more sophisticated. The use of machine learning in credit card fraud detection and mitigation has grown significantly. In this research study, a novel method for identifying fraudulent credit card transactions with machine learning algorithms is presented. The proposed system leverages past transaction data and various characteristics associated with each transaction, such as location, transaction amount, and time, to build a predictive model. These models are trained to recognize patterns that point to fraudulent activity using supervised learning methods like random forests and support vector machines. Several metrics, including accuracy, precision, recall, and F1 score, are used to assess the performance of the model. According to experimental data, the suggested method works better than conventional rule-based fraud detection systems and achieves high accuracy. The system can effectively detect fraudulent credit card transactions while minimizing false positives. Machine learning improves the security of credit card transactions by detecting fraud in real time. In summary, this study advances the field of credit card fraud detection by using machine learning algorithms to counteract the constantly changing tactics used by fraudsters.

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Symptom-Based Disease Prediction: A Machine Learning Approach

Sirigineedi,

Kumar,

Prakash

et al. 2024

JAIMLNN

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The advent of machine learning techniques has revolutionized various sectors, including healthcare. This project concentrates on leveraging machine learning algorithms for disease prediction based on symptoms. With a dataset comprising 132 symptoms and 41 diseases, the aim is to develop a robust predictive model capable of accurately diagnosing diseases given a set of symptoms. The process involves several key steps. Initially, the dataset is preprocessed to handle missing values, encode categorical variables, and normalize the data. To determine which symptoms are most pertinent to the prognosis of a disease, feature selection techniques are utilized. Various machine learning algorithms, including decision trees, support vector machines, random forests, and XGBoost, were explored to determine the most effective prediction model. XGBoost, in particular, emerges as one of the topperforming models because of its capacity to manage complicated relationships within the data and its effectiveness in handling imbalanced datasets. To evaluate the models' performance, evaluation criteria like accuracy, precision, recall, and F1-score are used. Moreover, to enhance model performance and avoid overfitting, techniques like crossvalidation and hyperparameter tuning are utilized. The proposed system holds significant potential in aiding healthcare professionals in diagnosing diseases promptly and accurately, thereby improving patient outcomes and reducing healthcare costs. It is important to note that the model needs further validation on diverse datasets and regular updates to remain relevant in clinical settings.

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Improving Fisheries Management through Deep learning based Automated fish counting

Cited by 3 publications

References 14 publications

White Leg Shrimp Larvae Counting System

White Leg Shrimp Larvae Counting System

Fake Credit Transaction Detection Using Machine Learning

Symptom-Based Disease Prediction: A Machine Learning Approach

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