A Survey on Cardiovascular Prediction using Variant Machine learning

Automation of medical image analysis helps medical practitioners to ensure early detection of certain diseases. Diabetic Retinopathy (DR) is a widespread condition of diabetes mellitus and a main global cause of vision impairment. The manual diagnosis of diabetic retinopathy by ophthalmologists requires a significant amount of time, causing inconvenience and discomfort for patients. However, the use of automated technology makes it possible to quickly identify diabetic retinopathy, permitting the continuation of therapy without interruption and averting future ocular damage. This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. The accuracy of various models is evaluated using the Cohen Kappa metric. The results of this study add a contribution to the research on the application of machine learning models for diagnosing diabetic retinopathy.

Section: Existing Methodsmentioning

confidence: 99%

Performance Comparison of CNN and DNN Algorithms for Automation of Diabetic Retinopathy Disease

Sankara Babu,

Mandapati,

Mandapati

et al. 2023

“…The second phase of the methodology gives crop recommendations by the developed model using the values obtained from the soil. Figure 2 represents the architecture diagram that shows the actual flow of execution of the methodology [9][10][11][12][13].…”

Section: Methodsmentioning

confidence: 99%

Sustainable Crop Recommendation System Using Soil NPK Sensor

Gottemukkala,

Jajala,

Thalari

et al. 2023

The effective management of nutrient resources in agricultural practices is crucial for optimizing crop yields and ensuring sustainable farming. Traditionally, farmers have relied on manual methods or expert knowledge to determine the appropriate amount and type of nutrients required by crops. However, these methods often lack precision and can lead to suboptimal fertilization, resulting in reduced productivity and environmental degradation. In recent years, advancements in sensor technology have paved the way for more accurate and efficient crop management systems. One such innovation is the NPK sensor, which enables real-time monitoring of soil nutrient levels. Our proposed system utilizes NPK sensor data to offer personalized fertilization recommendations to farmers. The system integrates sensor technology, machine learning algorithms, and agronomic expertise to provide precise and tailored nutrient recommendations based on the specific requirements of different crops and soil conditions. The system collects data from NPK sensors deployed in the field that includes soil nutrient levels. Machine learning algorithms analyze this data to identify patterns and correlation between nutrient levels and crop performance. By leveraging historical data and agronomic knowledge, the system can generate accurate and timely recommendations for nutrient application. In conclusion, the crop recommendation system presented here offers a novel approach to crop management by leveraging NPK sensor technology and machine learning. By providing accurate and personalized nutrient recommendations, the system has the potential to revolutionize modern agriculture, enhancing productivity while promoting environmental stewardship. Further research and field trials are needed to validate and refine the system’s performance and usability, but the preliminary results show promising potential for the adoption of such system in real-world agricultural settings.

“…These biases may lead to unfair outcomes in medical decision-making, genetic counseling, or drug development. Ensuring algorithmic fairness and addressing bias is crucial to avoid perpetuating health disparities [23].…”

Section: Data Sharing and Ownershipmentioning

confidence: 99%

Decoding the Human Genome: Machine Learning Techniques for DNA Sequencing Analysis

C.,

P.,

G.Y.

et al. 2023

The decoding of the human genome has been a landmark achievement in the field of genomics, generating vast amounts of DNA sequencing data that necessitate sophisticated analysis techniques. In recent years, machine learning has emerged as a powerful tool in unravelling the complexities of genomic data and expediting research discoveries. This article explores the integration of machine learning techniques in DNA sequencing analysis, elucidating their applications in genome assembly, variant calling, personalized medicine, and drug discovery. Additionally, it addresses the ethical considerations surrounding the use of genomic data. By harnessing the potential of machine learning, researchers are unlocking new insights into human genetics and paving the way for transformative advancements in healthcare and scientific understanding.