The problem to classify big data is an important one in machine learning. There are multiple ways to classify data, but the support vector machine (SVM) has become a great tool for the data scientist. In this paper we examine several modifications of the support vector machine algorithm that...
One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. (Adv Neural Inf Process Syst 29, 2016) proposed the notion of equality of opportunity (EO), which is compa...
In real-world public safety and law enforcement scenarios, Amazon Rekognition is almost exclusively used to help narrow the field and allow humans to expeditiously review and consider options using their judgment (and not to make fully autonomous decisions), where it can help find lost children, f...
In order to demonstrate the methodology behind the map in Eq. (1), we begin by describing the process of learning the CC energy directly via Eq. (2) based on a set of 102 random water geometries (Fig.1b and Supplementary Fig.1). Note that the mean absolute error (MAE) of DFT ener...
The growing use of machine learning in policy and social impact settings has raised concerns over fairness implications, especially for racial minorities. These concerns have generated considerable interest among machine learning and artificial intelligence researchers, who have developed new methods and esta...
Such capabilities enable a variety of potentials, unimaginable for earlier generation networks, notable examples being 5G built-in Machine Learning (ML) mechanisms for QoE estimation, subject of this paper. In this work, an ML-based mechanism for video streaming QoE estimation in 5G networks is ...
As a result, a way has been sought to train machine learning models without having to centralize all data into a central storage point. The concept of federated learning was first introduced by McMahan et al. [24] in 2016. Federated learning has received a lot of attention for its ability...
I’ve learnt these methods with experience. I’ve always preferred to learn practically than digging theories. And, my approach has always encouraged me. In this article, I’ve shared the 8 proven ways using which you can create a robust machine learning model. I hope my knowledge can help...
Restricted Boltzmann Machines (RBMs) offer a versatile architecture for unsupervised machine learning that can in principle approximate any target probability distribution with arbitrary accuracy. However, the RBM model is usually not directly accessible due to its computational complexity, and Markov-chain...
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain o