The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.
Read More...Mitigating open-set misclassification in a colorectal cancer detecting neural network
The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.
Read More...SmartZoo: A Deep Learning Framework for an IoT Platform in Animal Care
Zoos offer educational and scientific advantages but face high maintenance costs and challenges in animal care due to diverse species' habits. Challenges include tracking animals, detecting illnesses, and creating suitable habitats. We developed a deep learning framework called SmartZoo to address these issues and enable efficient animal monitoring, condition alerts, and data aggregation. We discovered that the data generated by our model is closer to real data than random data, and we were able to demonstrate that the model excels at generating data that resembles real-world data.
Read More...An explainable model for content moderation
The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.
Read More...Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes
In this article the authors created an interaction map of proteins involved in colorectal cancer to look for driver vs. non-driver genes. That is they wanted to see if they could determine what genes are more likely to drive the development and progression in colorectal cancer and which are present in altered states but not necessarily driving disease progression.
Read More...Using advanced machine learning and voice analysis features for Parkinson’s disease progression prediction
The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.
Read More...Artificial intelligence assisted violin performance learning
In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.
Read More...Effects of spices on rice spoilage
In this work, based on centuries of history where spices have been used and thought to have antimicrobial properties that prolong the shelf life of food, the authors investigated if several spices used in Indian cooking could delay the spoilage of cooked white rice. Based on changed in appearance and smell, as well as growth on agar plates, they found that cinnamon was the most effective in delaying spoilage, followed by cumin, pepper, garlic, and ginger. Their findings suggest the ability to use spices rather than chemical food preservatives to prolong the shelf life of foods.
Read More...How artificial intelligence deep learning models can be used to accurately determine lung cancers
The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.
Read More...Penalty kick success is unaffected by direction: Insights from right-footed world-class soccer players
The Effects of Knowledge, Lack of Knowledge, and Deception on Rate of Perceived Exertion and Performance During Workouts
In this study, the authors examine how knowledge, lack of knowledge, and deception affect the rate of perceived exertion and actual performance of teenagers in sprint training. Their results suggest that fully informing athletes about workout duration yields the fastest and most consistent speeds.
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