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Can AI solve the messy data predicament?

Messy data brings a does of reality to analytics projects everywhere - it's a problem for AI projects as well. But can AI allow us to turn the messy data. ....

George Fraser , Dharmesh Thakker , Attention Neural Network , Recurrent Neural Networks , Recurrent Convolutional Neural Networks , Recurrent Neural Networks Rnns , Natural Language Processing , Battery Ventures , Snowflake Face Off , Complete Active Data Catalog , Dataset Overlap Detection , Naive Bayes , Decision Tree , Logistic Regression , K Nearest Neighbor , Support Vector , Neural Networks , Short Term Memory , Neural Network , Expression Grammar , Expression Framework , Machine Learning , Knowledge Graph , Data Parsing , Enterprise Knowledge Graph , Structured Data Parsing ,

Immunodiagnosis: Unleashing the potential of personalized immunotherapy

Researchers discuss the concept of immunodiagnosis and explores the potential of artificial intelligence in ID systems for precise tumor immunotherapy. ....

Juan Gaertner Shutterstock , Image Credit , Juan Gaertner , Mass Index , Logistic Regression , Decision Trees , Support Vector Machines , Artificial Intelligence , Immune Response , Immune System , Pd L1 , Ars Cov 2 ,

"Early detection of Diabetes using Machine Learning Techniques" by Atef Hadi Ataya

Diabetes is understood to be an ailment where the human being system's blood sugar amounts tend to be unusually higher. It's acknowledged all over the globe among the long-term problems. Diabetes prevents your body's capability to help to make insulin, leading to extreme blood sugar levels as well as gluconeogenesis abnormalities. A lot of women are influenced by gestational diabetes, the industry type of diabetes occurring throughout being pregnant. Ladies tend to be more likely compared to guys to build up diabetes-related difficulties, as well as women that are pregnant may create gestational diabetes throughout their pregnancy. The recent advancement of Machine learning (ML) provides a significant part in illness detection and prediction upon many phenomena. This makes ML great techniques to predict diabetic disease prediction. This research chose the well-known Logistic Regression (LgR), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forests (RF), ....

Logistic Regression , Nearest Neighbors , Support Vector Machine , Random Forests , Artificial Intelligence , Disease Detection , Machine Learning ,

"AI Solution to Assist Online Education Productivity via Personalizing " by M. L.A.P. Liyanage, U. J. Hirimuthugoda et al.

Higher productivity in online education can be attained by consistent student engagement and appropriate use of learning resources and methodologies in the form of audio, video, and text. Lower literacy rates, decreased popularity, and unsatisfactory end-user goals can result from unbalanced or inappropriate use of the aforementioned. Prior studies mainly focused on identifying and separating the elements affecting the quality of online education and pinpointing the students' preferred learning styles outside of in-person and online instruction. This has not been able to clearly show how to enhance and customize the online learning environment in order to benefit the aforementioned criteria. This case study will primarily concentrate on elements that can be personalized and optimized to improve the quality of online education. With the aid of various algorithms like logistic regression,Support Vector Machines (SVM), time series forecasting (ARIMA), deep neural networks, and Recurr ....

Recurrent Neural Networks , Support Vector Machines , Learning Management System , Deep Learning , Deep Neural Networks , Logistic Regression , Machine Learning , Online Education , Support Vector Machines ,

"Deep Learning Metaphor Detection with Emotion-Cognition Association" by Md Saifullah Razali, Alfian Abdul Halin et al.

The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and pe ....

Convolutional Neural Network , Support Vector Machine , Logistic Regression , Decision Tree , K Nearest Neighbour , Deep Learning , Etaphor Detection , Natural Language Processing ,